diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f06c16c2..f1c3dc31 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -10,24 +10,6 @@ # Pull Requests -We'd love to accept your patches and contributions to this project. There are -just a few small guidelines you need to follow. - -## Contributor License Agreement - -Contributions to this project must be accompanied by a Contributor License -Agreement. You (or your employer) retain the copyright to your contribution, -this simply gives us permission to use and redistribute your contributions as -part of the project. Head over to to see -your current agreements on file or to sign a new one. - -You generally only need to submit a CLA once, so if you've already submitted one -(even if it was for a different project), you probably don't need to do it -again. - -## Code reviews - -All submissions, including submissions by project members, require review. We -use GitHub pull requests for this purpose. Consult -[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more -information on using pull requests. +Due to lack of bandwidth, we are not accepting pull requests at this time. If +there is something you feel should be added to the repo, please raise an issue +or feature request. diff --git a/README.md b/README.md index 3943daf5..4548b7b5 100644 --- a/README.md +++ b/README.md @@ -30,6 +30,7 @@ Dopamine supports the following agents, implemented with jax: * Rainbow ([Hessel et al., 2018][rainbow]) * IQN ([Dabney et al., 2018][iqn]) * SAC ([Haarnoja et al., 2018][sac]) +* PPO ([Schulman et al., 2017][ppo]) For more information on the available agents, see the [docs](https://google.github.io/dopamine/docs). @@ -140,6 +141,8 @@ Conference on Learning Representations, 2016.][prioritized_replay] [Haarnoja et al., *Soft Actor-Critic Algorithms and Applications*, arXiv preprint arXiv:1812.05905, 2018.][sac] +[Schulman et al., *Proximal Policy Optimization Algorithms*.][ppo] + ## Giving credit If you use Dopamine in your work, we ask that you cite our @@ -160,7 +163,6 @@ If you use Dopamine in your work, we ask that you cite our ``` - [docs]: https://google.github.io/dopamine/docs/ [baselines]: https://google.github.io/dopamine/baselines [machado]: https://jair.org/index.php/jair/article/view/11182 @@ -172,5 +174,6 @@ If you use Dopamine in your work, we ask that you cite our [rainbow]: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/download/17204/16680 [iqn]: https://arxiv.org/abs/1806.06923 [sac]: https://arxiv.org/abs/1812.05905 +[ppo]: https://arxiv.org/abs/1707.06347 [dopamine_paper]: https://arxiv.org/abs/1812.06110 [vitualenv]: https://docs.python.org/3/library/venv.html#creating-virtual-environments diff --git a/baselines/atari/README.md b/baselines/atari/README.md index 8003c4a6..6e0b7bd2 100644 --- a/baselines/atari/README.md +++ b/baselines/atari/README.md @@ -1,18 +1,40 @@ # Baseline data This directory provides information about the baseline data provided by -Dopamine. The default hyperparameter configuration for the 4 agents we are +Dopamine. The default hyperparameter configuration for the agents we are providing yields a standardized "apples to apples" comparison between them. -The default configuration files files for each agent (set up with -[gin configuration framework](https://github.com/google/gin-config)) are: +The default configuration files files for these agents (set up with [gin +configuration framework](https://github.com/google/gin-config)) are: -* [`dopamine/agents/dqn/configs/dqn.gin`](https://github.com/google/dopamine/blob/master/dopamine/agents/dqn/configs/dqn.gin) -* [`dopamine/agents/rainbow/configs/c51.gin`](https://github.com/google/dopamine/blob/master/dopamine/agents/rainbow/configs/c51.gin) -* [`dopamine/agents/rainbow/configs/rainbow.gin`](https://github.com/google/dopamine/blob/master/dopamine/agents/rainbow/configs/rainbow.gin) -* [`dopamine/agents/implicit_quantile/configs/implicit_quantile.gin`](https://github.com/google/dopamine/blob/master/dopamine/agents/implicit_quantile/configs/implicit_quantile.gin) +* [`dopamine/jax/agents/dqn/configs/dqn.gin`](https://github.com/google/dopamine/blob/master/dopamine/jax/agents/dqn/configs/dqn.gin) +* [`dopamine/jax/agents/implicit_quantile/configs/implicit_quantile.gin`](https://github.com/google/dopamine/blob/master/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile.gin) +* [`dopamine/jax/agents/quantile/configs/quantile.gin`](https://github.com/google/dopamine/blob/master/dopamine/jax/agents/quantile/configs/quantile.gin) +* [`dopamine/jax/agents/rainbow/configs/rainbow.gin`](https://github.com/google/dopamine/blob/master/dopamine/jax/agents/rainbow/configs/rainbow.gin) -## Hyperparemeter comparison +## Visualization +We provide a [website](https://google.github.io/dopamine/baselines/atari/plots.html) +where you can quickly visualize the training runs for all our default agents. + +The plots are rendered from a set of +[JSON files](https://github.com/google/dopamine/tree/master/baselines/atari/data) +which we compiled. These may prove useful in their own right to compare +against results obtained from other frameworks. + +## Legacy TensorFlow models + +Dopamine agents originally used [TensorFlow](https://www.tensorflow.org/) for +its networks and agents, but has since migrated to +[Jax](https://jax.readthedocs.io/en/latest/). The default configuration files +files for the legacy TF agents (set up with [gin configuration +framework](https://github.com/google/gin-config)) are: + +* [`dopamine/tf/agents/dqn/configs/dqn.gin`](https://github.com/google/dopamine/blob/master/dopamine/tf/agents/dqn/configs/dqn.gin) +* [`dopamine/tf/agents/rainbow/configs/c51.gin`](https://github.com/google/dopamine/blob/master/dopamine/tf/agents/rainbow/configs/c51.gin) +* [`dopamine/tf/agents/rainbow/configs/rainbow.gin`](https://github.com/google/dopamine/blob/master/dopamine/tf/agents/rainbow/configs/rainbow.gin) +* [`dopamine/tf/agents/implicit_quantile/configs/implicit_quantile.gin`](https://github.com/google/dopamine/blob/master/dopamine/agents/tf/implicit_quantile/configs/implicit_quantile.gin) + +### Hyperparemeter comparison Our results compare the agents with the same hyperparameters: target network update frequency, frequency at which exploratory actions are selected (ε), the length of the schedule over which ε is annealed, and the number of agent steps @@ -23,6 +45,9 @@ actions instead of 10% (as used in the original Nature paper). Step size and optimizer were taken as published. The table below summarizes our choices. All numbers are in ALE frames. +Note that these numbers were obtained with the legacy TensorFlow +implementations. + | | Our baseline results | [DQN][dqn] | [C51][c51] | [Rainbow][rainbow] | [IQN][iqn] | | :---------------------------------- | :------------------: | :--------: | :--------: | :----------------: | :--------: | | **Training ε** | 0.01 | 0.1 | 0.01 | 0.01 | 0.01 | @@ -31,17 +56,9 @@ numbers are in ALE frames. | **Min. history to start learning** | 80,000 frames | 200,000 frames | 200,000 frames | 80,000 frames | 200,000 frames | | **Target network update frequency** | 32,000 frames | 40,000 frames | 40,000 frames | 32,000 frames | 40,000 frames | -## Visualization -We provide a [website](https://google.github.io/dopamine/baselines/atari/plots.html) -where you can quickly visualize the training runs for all our default agents. - -The plots are rendered from a set of -[JSON files](https://github.com/google/dopamine/tree/master/baselines/atari/data) -which we compiled. These may prove useful in their own right to compare -against results obtained from other frameworks. - [dqn]: https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf [c51]: https://arxiv.org/abs/1707.06887 [rainbow]: https://arxiv.org/abs/1710.02298 +[qr-dqn]: https://arxiv.org/abs/1710.10044 [iqn]: https://arxiv.org/abs/1806.06923 diff --git a/baselines/atari/data/airraid_CNN.json b/baselines/atari/data/airraid_CNN.json new file mode 100644 index 00000000..74271b3a --- /dev/null +++ b/baselines/atari/data/airraid_CNN.json @@ -0,0 +1,25002 @@ +[ + { + "Iteration": 0, + "Value": 546.1458333333334, + "Agent": "Rainbow" + }, + { + "Iteration": 0, + "Value": 558.5129310344828, + "Agent": "Rainbow" + }, + { + "Iteration": 0, + "Value": 462.4773960216998, + "Agent": "Rainbow" + }, + { + "Iteration": 0, + "Value": 536.7283950617284, + "Agent": "Rainbow" + }, + { + "Iteration": 0, + "Value": 486.63127413127415, + "Agent": "Rainbow" + }, + { + "Iteration": 1, + "Value": 897.96875, 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+ { + "Iteration": 199, + "Value": 15045.555555555555, + "Agent": "IQN" + }, + { + "Iteration": 199, + "Value": 12906.382978723404, + "Agent": "IQN" + } +] \ No newline at end of file diff --git a/baselines/atari/data/zaxxon_Impala.vg.json b/baselines/atari/data/zaxxon_Impala.vg.json new file mode 100644 index 00000000..57a02ff7 --- /dev/null +++ b/baselines/atari/data/zaxxon_Impala.vg.json @@ -0,0 +1 @@ +{"$schema": "https://vega.github.io/schema/vega-lite/v3.json", "data": {"url": "data/zaxxon_Impala.json"}, "height": 400, "width": 800, "encoding": {"color": {"field": "Agent", "type": "nominal"}, "x": {"field": "Iteration", "type": "quantitative"}}, "layer": [{"encoding": {"opacity": {"value": 0.3}, "y": {"aggregate": "ci0", "title": "Return (95% CIs)", "field": "Value", "type": "quantitative"}, "y2": {"aggregate": "ci1", "field": "Value"}}, "mark": "area"}, {"encoding": {"y": {"aggregate": "mean", "field": "Value", "type": "quantitative"}}, "mark": "line"}], "padding": 5} \ No newline at end of file diff --git a/baselines/atari/legacy_plots.html b/baselines/atari/legacy_plots.html new file mode 100644 index 00000000..66d05a37 --- /dev/null +++ b/baselines/atari/legacy_plots.html @@ -0,0 +1,134 @@ + + + + + + + + + + + + + +

(Legacy) Baseline plots

+

This page provides a quick visualization of the training runs for all our legacy TensorFlow (and some Jax) agents.

+ +

See this page for the latest runs.

+
+ +
+ + + diff --git a/baselines/atari/plots.html b/baselines/atari/plots.html index 6bbbad6c..f229d86d 100644 --- a/baselines/atari/plots.html +++ b/baselines/atari/plots.html @@ -1,5 +1,5 @@ + +## Primary symbols +* dopamine +* dopamine.colab +* dopamine.colab.utils +* dopamine.colab.utils.get_latest_file +* dopamine.colab.utils.get_latest_iteration +* dopamine.colab.utils.load_baselines +* dopamine.colab.utils.load_statistics +* dopamine.colab.utils.read_experiment +* dopamine.colab.utils.summarize_data +* dopamine.continuous_domains +* dopamine.continuous_domains.run_experiment +* dopamine.continuous_domains.run_experiment.ContinuousRunner +* dopamine.continuous_domains.run_experiment.ContinuousTrainRunner +* dopamine.continuous_domains.run_experiment.create_continuous_agent +* dopamine.continuous_domains.run_experiment.create_continuous_runner +* dopamine.discrete_domains +* dopamine.discrete_domains.atari_lib +* dopamine.discrete_domains.atari_lib.AtariPreprocessing +* dopamine.discrete_domains.atari_lib.GameOverWrapper +* dopamine.discrete_domains.atari_lib.ImplicitQuantileNetwork +* dopamine.discrete_domains.atari_lib.NatureDQNNetwork +* dopamine.discrete_domains.atari_lib.RainbowNetwork +* dopamine.discrete_domains.atari_lib.create_atari_environment +* dopamine.discrete_domains.checkpointer +* dopamine.discrete_domains.checkpointer.Checkpointer +* dopamine.discrete_domains.checkpointer.get_latest_checkpoint_number +* dopamine.discrete_domains.gym_lib +* dopamine.discrete_domains.gym_lib.AcrobotDQNNetwork +* dopamine.discrete_domains.gym_lib.AcrobotFourierDQNNetwork +* dopamine.discrete_domains.gym_lib.AcrobotRainbowNetwork +* dopamine.discrete_domains.gym_lib.BasicDiscreteDomainNetwork +* dopamine.discrete_domains.gym_lib.CartpoleDQNNetwork +* dopamine.discrete_domains.gym_lib.CartpoleFourierDQNNetwork +* dopamine.discrete_domains.gym_lib.CartpoleRainbowNetwork +* dopamine.discrete_domains.gym_lib.FourierBasis +* dopamine.discrete_domains.gym_lib.FourierDQNNetwork +* dopamine.discrete_domains.gym_lib.GymPreprocessing +* dopamine.discrete_domains.gym_lib.LunarLanderDQNNetwork +* dopamine.discrete_domains.gym_lib.MountainCarDQNNetwork +* dopamine.discrete_domains.gym_lib.create_gym_environment +* dopamine.discrete_domains.iteration_statistics +* dopamine.discrete_domains.iteration_statistics.IterationStatistics +* dopamine.discrete_domains.legacy_networks +* dopamine.discrete_domains.legacy_networks.acrobot_dqn_network +* dopamine.discrete_domains.legacy_networks.acrobot_fourier_dqn_network +* dopamine.discrete_domains.legacy_networks.acrobot_rainbow_network +* dopamine.discrete_domains.legacy_networks.cartpole_dqn_network +* dopamine.discrete_domains.legacy_networks.cartpole_fourier_dqn_network +* dopamine.discrete_domains.legacy_networks.cartpole_rainbow_network +* dopamine.discrete_domains.legacy_networks.fourier_dqn_network +* dopamine.discrete_domains.legacy_networks.implicit_quantile_network +* dopamine.discrete_domains.legacy_networks.nature_dqn_network +* dopamine.discrete_domains.legacy_networks.rainbow_network +* dopamine.discrete_domains.logger +* dopamine.discrete_domains.logger.Logger +* dopamine.discrete_domains.run_experiment +* dopamine.discrete_domains.run_experiment.Runner +* dopamine.discrete_domains.run_experiment.TrainRunner +* dopamine.discrete_domains.run_experiment.create_agent +* dopamine.discrete_domains.run_experiment.create_runner +* dopamine.discrete_domains.run_experiment.load_gin_configs +* dopamine.discrete_domains.train +* dopamine.google +* dopamine.google.experiments +* dopamine.google.experiments.agent_registry +* dopamine.google.experiments.example_experiment +* dopamine.google.experiments.example_experiment.agents +* dopamine.google.experiments.example_experiment.example_dqn_agent +* dopamine.google.experiments.moes_reset +* dopamine.google.experiments.moes_reset.agents +* dopamine.google.experiments.moes_reset.agents.dqn_moe_agent +* dopamine.google.experiments.moes_reset.agents.full_rainbow_moe_agent +* dopamine.google.experiments.moes_reset.architectures +* dopamine.google.experiments.moes_reset.architectures.expert_reset +* dopamine.google.experiments.moes_reset.architectures.types +* dopamine.google.experiments.two_hot +* dopamine.google.experiments.two_hot.dqn_multihead_agent +* dopamine.google.experiments.two_hot.dqn_two_hot_agent +* dopamine.google.experiments.two_hot.losses +* dopamine.google.experiments.two_hot.transforms +* dopamine.google.metrics +* dopamine.google.metrics.datatable_collector +* dopamine.google.metrics.hlog_collector +* dopamine.google.metrics.multiscope_collector +* dopamine.google.xm_utils +* dopamine.jax +* dopamine.jax.agents +* dopamine.jax.agents.dqn +* dopamine.jax.agents.dqn.dqn_agent +* dopamine.jax.agents.dqn.dqn_agent.JaxDQNAgent +* dopamine.jax.agents.full_rainbow +* dopamine.jax.agents.full_rainbow.full_rainbow_agent +* dopamine.jax.agents.full_rainbow.full_rainbow_agent.JaxFullRainbowAgent +* dopamine.jax.agents.implicit_quantile +* dopamine.jax.agents.implicit_quantile.implicit_quantile_agent +* dopamine.jax.agents.implicit_quantile.implicit_quantile_agent.JaxImplicitQuantileAgent +* dopamine.jax.agents.ppo +* dopamine.jax.agents.ppo.ppo_agent +* dopamine.jax.agents.ppo.ppo_agent.PPOAgent +* dopamine.jax.agents.quantile +* dopamine.jax.agents.quantile.quantile_agent +* dopamine.jax.agents.quantile.quantile_agent.JaxQuantileAgent +* dopamine.jax.agents.rainbow +* dopamine.jax.agents.rainbow.rainbow_agent +* dopamine.jax.agents.rainbow.rainbow_agent.JaxRainbowAgent +* dopamine.jax.agents.rainbow.rainbow_agent.project_distribution +* dopamine.jax.agents.sac +* dopamine.jax.agents.sac.sac_agent +* dopamine.jax.agents.sac.sac_agent.SACAgent +* dopamine.jax.checkpointers +* dopamine.jax.checkpointers.CheckpointHandler +* dopamine.jax.checkpointers.Checkpointable +* dopamine.jax.checkpointers.Checkpointer +* dopamine.jax.continuous_networks +* dopamine.jax.continuous_networks.ActorCriticNetwork +* dopamine.jax.continuous_networks.ActorCriticOutput +* dopamine.jax.continuous_networks.ActorNetwork +* dopamine.jax.continuous_networks.ActorOutput +* dopamine.jax.continuous_networks.CriticNetwork +* dopamine.jax.continuous_networks.CriticOutput +* dopamine.jax.continuous_networks.PPOActorCriticNetwork +* dopamine.jax.continuous_networks.PPOActorCriticOutput +* dopamine.jax.continuous_networks.PPOActorNetwork +* dopamine.jax.continuous_networks.PPOActorOutput +* dopamine.jax.continuous_networks.PPOCriticNetwork +* dopamine.jax.continuous_networks.PPOCriticOutput +* dopamine.jax.losses +* dopamine.jax.losses.huber_loss +* dopamine.jax.losses.mse_loss +* dopamine.jax.losses.softmax_cross_entropy_loss_with_logits +* dopamine.jax.networks +* dopamine.jax.networks.ClassicControlDQNNetwork +* dopamine.jax.networks.ClassicControlRainbowNetwork +* dopamine.jax.networks.FourierBasis +* dopamine.jax.networks.FullRainbowNetwork +* dopamine.jax.networks.ImpalaDQNNetwork +* dopamine.jax.networks.ImpalaEncoder +* dopamine.jax.networks.ImplicitQuantileNetwork +* dopamine.jax.networks.JaxFourierDQNNetwork +* dopamine.jax.networks.NatureDQNNetwork +* dopamine.jax.networks.NoisyNetwork +* dopamine.jax.networks.PPOActorNetwork +* dopamine.jax.networks.PPOCriticNetwork +* dopamine.jax.networks.PPODiscreteActorCriticNetwork +* dopamine.jax.networks.PPOSharedNetwork +* dopamine.jax.networks.QuantileNetwork +* dopamine.jax.networks.RainbowNetwork +* dopamine.jax.networks.Stack +* dopamine.jax.networks.feature_layer +* dopamine.jax.networks.preprocess_atari_inputs +* dopamine.jax.replay_memory +* dopamine.jax.replay_memory.accumulator +* dopamine.jax.replay_memory.accumulator.Accumulator +* dopamine.jax.replay_memory.accumulator.TransitionAccumulator +* dopamine.jax.replay_memory.elements +* dopamine.jax.replay_memory.elements.ReplayElement +* dopamine.jax.replay_memory.elements.ReplayElementProtocol +* dopamine.jax.replay_memory.elements.TransitionElement +* dopamine.jax.replay_memory.elements.compress +* dopamine.jax.replay_memory.elements.uncompress +* dopamine.jax.replay_memory.replay_buffer +* dopamine.jax.replay_memory.replay_buffer.ReplayBuffer +* dopamine.jax.replay_memory.samplers +* dopamine.jax.replay_memory.samplers.PrioritizedSampleMetadata +* dopamine.jax.replay_memory.samplers.PrioritizedSamplingDistribution +* dopamine.jax.replay_memory.samplers.SampleMetadata +* dopamine.jax.replay_memory.samplers.SamplingDistribution +* dopamine.jax.replay_memory.samplers.SequentialSamplingDistribution +* dopamine.jax.replay_memory.samplers.UniformSamplingDistribution +* dopamine.jax.replay_memory.sum_tree +* dopamine.jax.replay_memory.sum_tree.SumTree +* dopamine.jax.serialization +* dopamine.jax.serialization.LongIntegerEncoding +* dopamine.jax.serialization.NumpyEncoding +* dopamine.jax.serialization.decode +* dopamine.jax.serialization.encode +* dopamine.labs +* dopamine.labs.atari_100k +* dopamine.labs.atari_100k.atari_100k_rainbow_agent +* dopamine.labs.cale +* dopamine.labs.cale.ppo_cale +* dopamine.labs.cale.sac_cale +* dopamine.labs.cale.utils +* dopamine.labs.moes +* dopamine.labs.moes.agents +* dopamine.labs.moes.agents.dqn_moe_agent +* dopamine.labs.moes.agents.full_rainbow_moe_agent +* dopamine.labs.moes.agents.losses +* dopamine.labs.moes.agents.rainbow_100k_moe_agent +* dopamine.labs.moes.agents.types +* dopamine.labs.moes.architectures +* dopamine.labs.moes.architectures.moe +* dopamine.labs.moes.architectures.networks +* dopamine.labs.moes.architectures.routers +* dopamine.labs.moes.architectures.softmoe +* dopamine.labs.moes.architectures.types +* dopamine.labs.moes.google +* dopamine.labs.moes.google.dmoe +* dopamine.labs.sac_from_pixels +* dopamine.labs.sac_from_pixels.continuous_networks +* dopamine.labs.sac_from_pixels.deepmind_control_lib +* dopamine.metrics +* dopamine.metrics.collector +* dopamine.metrics.collector.Collector +* dopamine.metrics.collector_dispatcher +* dopamine.metrics.collector_dispatcher.CollectorDispatcher +* dopamine.metrics.collector_dispatcher.add_collector +* dopamine.metrics.console_collector +* dopamine.metrics.console_collector.ConsoleCollector +* dopamine.metrics.pickle_collector +* dopamine.metrics.pickle_collector.PickleCollector +* dopamine.metrics.statistics_instance +* dopamine.metrics.statistics_instance.StatisticsInstance +* dopamine.metrics.tensorboard_collector +* dopamine.metrics.tensorboard_collector.TensorboardCollector +* dopamine.tf +* dopamine.tf.agents +* dopamine.tf.agents.dqn +* dopamine.tf.agents.dqn.dqn_agent +* dopamine.tf.agents.dqn.dqn_agent.DQNAgent +* dopamine.tf.agents.implicit_quantile +* dopamine.tf.agents.implicit_quantile.implicit_quantile_agent +* dopamine.tf.agents.implicit_quantile.implicit_quantile_agent.ImplicitQuantileAgent +* dopamine.tf.agents.rainbow +* dopamine.tf.agents.rainbow.rainbow_agent +* dopamine.tf.agents.rainbow.rainbow_agent.RainbowAgent +* dopamine.tf.agents.rainbow.rainbow_agent.project_distribution +* dopamine.tf.replay_memory +* dopamine.tf.replay_memory.circular_replay_buffer +* dopamine.tf.replay_memory.circular_replay_buffer.OutOfGraphReplayBuffer +* dopamine.tf.replay_memory.circular_replay_buffer.ReplayElement +* dopamine.tf.replay_memory.circular_replay_buffer.WrappedReplayBuffer +* dopamine.tf.replay_memory.prioritized_replay_buffer +* dopamine.tf.replay_memory.prioritized_replay_buffer.OutOfGraphPrioritizedReplayBuffer +* dopamine.tf.replay_memory.prioritized_replay_buffer.WrappedPrioritizedReplayBuffer +* dopamine.tf.replay_memory.sum_tree +* dopamine.tf.replay_memory.sum_tree.SumTree +* dopamine.utils +* dopamine.utils.agent_visualizer +* dopamine.utils.agent_visualizer.AgentVisualizer +* dopamine.utils.atari_plotter +* dopamine.utils.atari_plotter.AtariPlotter +* dopamine.utils.bar_plotter +* dopamine.utils.bar_plotter.BarPlotter +* dopamine.utils.example_viz +* dopamine.utils.example_viz.example_viz_lib +* dopamine.utils.example_viz.example_viz_lib.create_runner +* dopamine.utils.example_viz_lib +* dopamine.utils.example_viz_lib.create_runner +* dopamine.utils.line_plotter +* dopamine.utils.line_plotter.LinePlotter +* dopamine.utils.plotter +* dopamine.utils.plotter.Plotter + diff --git a/docs/api_docs/python/dopamine/api_report.pb b/docs/api_docs/python/dopamine/api_report.pb new file mode 100644 index 00000000..7e1dfece Binary files /dev/null and b/docs/api_docs/python/dopamine/api_report.pb differ diff --git a/docs/api_docs/python/dopamine/continuous_domains.md b/docs/api_docs/python/dopamine/continuous_domains.md new file mode 100644 index 00000000..ba97980e --- /dev/null +++ b/docs/api_docs/python/dopamine/continuous_domains.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.continuous_domains + + + + + + + + + + + + + +## Modules + +[`run_experiment`](../dopamine/continuous_domains/run_experiment.md) module: Module defining classes and helper methods for general agents. + diff --git a/docs/api_docs/python/dopamine/continuous_domains/run_experiment.md b/docs/api_docs/python/dopamine/continuous_domains/run_experiment.md new file mode 100644 index 00000000..d3c7425d --- /dev/null +++ b/docs/api_docs/python/dopamine/continuous_domains/run_experiment.md @@ -0,0 +1,38 @@ +description: Module defining classes and helper methods for general agents. + +
+ + +
+ +# Module: dopamine.continuous_domains.run_experiment + + + + + + + + + +Module defining classes and helper methods for general agents. + + + +## Classes + +[`class ContinuousRunner`](../../dopamine/continuous_domains/run_experiment/ContinuousRunner.md): Object that handles running Dopamine experiments. + +[`class ContinuousTrainRunner`](../../dopamine/continuous_domains/run_experiment/ContinuousTrainRunner.md): Object that handles running experiments. + +## Functions + +[`create_continuous_agent(...)`](../../dopamine/continuous_domains/run_experiment/create_continuous_agent.md): Creates an agent. + +[`create_continuous_runner(...)`](../../dopamine/continuous_domains/run_experiment/create_continuous_runner.md): Creates an experiment Runner. + diff --git a/docs/api_docs/python/dopamine/continuous_domains/run_experiment/ContinuousRunner.md b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/ContinuousRunner.md new file mode 100644 index 00000000..8ed1bb35 --- /dev/null +++ b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/ContinuousRunner.md @@ -0,0 +1,31 @@ +description: Object that handles running Dopamine experiments. + +
+ + +
+ +# dopamine.continuous_domains.run_experiment.ContinuousRunner + + + + + + + + + +Object that handles running Dopamine experiments. + +Inherits From: [`Runner`](../../../dopamine/discrete_domains/run_experiment/Runner.md) + + + +This is mostly the same as discrete_domains.Runner, but is written solely for +JAX/Flax agents. + diff --git a/docs/api_docs/python/dopamine/continuous_domains/run_experiment/ContinuousTrainRunner.md b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/ContinuousTrainRunner.md new file mode 100644 index 00000000..ab3d6727 --- /dev/null +++ b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/ContinuousTrainRunner.md @@ -0,0 +1,31 @@ +description: Object that handles running experiments. + +
+ + +
+ +# dopamine.continuous_domains.run_experiment.ContinuousTrainRunner + + + + + + + + + +Object that handles running experiments. + +Inherits From: [`ContinuousRunner`](../../../dopamine/continuous_domains/run_experiment/ContinuousRunner.md), [`Runner`](../../../dopamine/discrete_domains/run_experiment/Runner.md) + + + +This is mostly the same as discrete_domains.TrainRunner, but is written solely +for JAX/Flax agents. + diff --git a/docs/api_docs/python/dopamine/continuous_domains/run_experiment/create_continuous_agent.md b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/create_continuous_agent.md new file mode 100644 index 00000000..23f2e0dd --- /dev/null +++ b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/create_continuous_agent.md @@ -0,0 +1,99 @@ +description: Creates an agent. + +
+ + +
+ +# dopamine.continuous_domains.run_experiment.create_continuous_agent + + + + + + + + + +Creates an agent. + + +
+dopamine.continuous_domains.run_experiment.create_continuous_agent(
+    environment: dopamine.discrete_domains.gym_lib.GymPreprocessing,
+    agent_name: str,
+    summary_writer: Optional[tensorboard.SummaryWriter] = None
+) -> dopamine.jax.agents.dqn.dqn_agent.JaxDQNAgent
+
+ + + + + + + + + + + + + + + + + + + + + +

Args

+`environment` + + A gym environment. +
+`agent_name` + +str, name of the agent to create. +
+`summary_writer` + +A Tensorflow summary writer to pass to the agent for +in-agent training statistics in Tensorboard. +
+ + + + + + + + + + + +

Returns

+An RL agent. +
+ + + + + + + + + + + + +

Raises

+`ValueError` + +If `agent_name` is not in supported list. +
+ diff --git a/docs/api_docs/python/dopamine/continuous_domains/run_experiment/create_continuous_runner.md b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/create_continuous_runner.md new file mode 100644 index 00000000..c5c07c4c --- /dev/null +++ b/docs/api_docs/python/dopamine/continuous_domains/run_experiment/create_continuous_runner.md @@ -0,0 +1,92 @@ +description: Creates an experiment Runner. + +
+ + +
+ +# dopamine.continuous_domains.run_experiment.create_continuous_runner + + + + + + + + + +Creates an experiment Runner. + + +
+dopamine.continuous_domains.run_experiment.create_continuous_runner(
+    base_dir, schedule='continuous_train_and_eval'
+)
+
+ + + + + + + + + + + + + + + + + + +

Args

+`base_dir` + +str, base directory for hosting all subdirectories. +
+`schedule` + +string, which type of Runner to use. +
+ + + + + + + + + + + + +

Returns

+`runner` + +A `Runner` like object. +
+ + + + + + + + + + + + +

Raises

+`ValueError` + +When an unknown schedule is encountered. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/atari_lib/GameOverWrapper.md b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/GameOverWrapper.md new file mode 100644 index 00000000..59b79f3a --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/GameOverWrapper.md @@ -0,0 +1,27 @@ +description: A Wrapper class around Gym environments adding game over signal. + +
+ + +
+ +# dopamine.discrete_domains.atari_lib.GameOverWrapper + + + + + + + + + +A Wrapper class around Gym environments adding game over signal. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/atari_lib/ImplicitQuantileNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/ImplicitQuantileNetwork.md new file mode 100644 index 00000000..adec9727 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/ImplicitQuantileNetwork.md @@ -0,0 +1,27 @@ +description: The Implicit Quantile Network (Dabney et al., 2018).. + +
+ + +
+ +# dopamine.discrete_domains.atari_lib.ImplicitQuantileNetwork + + + + + + + + + +The Implicit Quantile Network (Dabney et al., 2018).. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/atari_lib/NatureDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/NatureDQNNetwork.md new file mode 100644 index 00000000..36e2ede2 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/NatureDQNNetwork.md @@ -0,0 +1,27 @@ +description: The convolutional network used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.atari_lib.NatureDQNNetwork + + + + + + + + + +The convolutional network used to compute the agent's Q-values. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/atari_lib/RainbowNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/RainbowNetwork.md new file mode 100644 index 00000000..d3a3136d --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/atari_lib/RainbowNetwork.md @@ -0,0 +1,27 @@ +description: The convolutional network used to compute agent's return distributions. + +
+ + +
+ +# dopamine.discrete_domains.atari_lib.RainbowNetwork + + + + + + + + + +The convolutional network used to compute agent's return distributions. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/checkpointer/get_latest_checkpoint_number.md b/docs/api_docs/python/dopamine/discrete_domains/checkpointer/get_latest_checkpoint_number.md new file mode 100644 index 00000000..f30d367a --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/checkpointer/get_latest_checkpoint_number.md @@ -0,0 +1,83 @@ +description: Returns the version number of the latest completed checkpoint. + +
+ + +
+ +# dopamine.discrete_domains.checkpointer.get_latest_checkpoint_number + + + + + + + + + +Returns the version number of the latest completed checkpoint. + + +
+dopamine.discrete_domains.checkpointer.get_latest_checkpoint_number(
+    base_directory,
+    override_number=None,
+    sentinel_file_identifier='checkpoint'
+)
+
+ + + + + + + + + + + + + + + + + + + + + +

Args

+`base_directory` + +str, directory in which to look for checkpoint files. +
+`override_number` + +None or int, allows the user to manually override the +checkpoint number via a gin-binding. +
+`sentinel_file_identifier` + +str, prefix used by checkpointer for naming +sentinel files. +
+ + + + + + + + + + + +

Returns

+int, the iteration number of the latest checkpoint, or -1 if none was found. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotDQNNetwork.md new file mode 100644 index 00000000..5514eb52 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotDQNNetwork.md @@ -0,0 +1,27 @@ +description: Keras DQN network for Acrobot. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.AcrobotDQNNetwork + + + + + + + + + +Keras DQN network for Acrobot. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotFourierDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotFourierDQNNetwork.md new file mode 100644 index 00000000..e00d133c --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotFourierDQNNetwork.md @@ -0,0 +1,29 @@ +description: Keras fourier DQN network for Acrobot. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.AcrobotFourierDQNNetwork + + + + + + + + + +Keras fourier DQN network for Acrobot. + +Inherits From: [`FourierDQNNetwork`](../../../dopamine/discrete_domains/gym_lib/FourierDQNNetwork.md) + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotRainbowNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotRainbowNetwork.md new file mode 100644 index 00000000..478befa7 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/AcrobotRainbowNetwork.md @@ -0,0 +1,27 @@ +description: Keras Rainbow network for Acrobot. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.AcrobotRainbowNetwork + + + + + + + + + +Keras Rainbow network for Acrobot. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/BasicDiscreteDomainNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/BasicDiscreteDomainNetwork.md new file mode 100644 index 00000000..47cad9bf --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/BasicDiscreteDomainNetwork.md @@ -0,0 +1,39 @@ +description: The fully connected network used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.BasicDiscreteDomainNetwork + + + + + + + + + +The fully connected network used to compute the agent's Q-values. + + + +This sub network used within various other models. Since it is an inner +block, we define it as a layer. These sub networks normalize their inputs to +lie in range [-1, 1], using min_/max_vals. It supports both DQN- and +Rainbow- style networks. +Attributes: + min_vals: float, minimum attainable values (must be same shape as `state`). + max_vals: float, maximum attainable values (must be same shape as `state`). + num_actions: int, number of actions. + num_atoms: int or None, if None will construct a DQN-style network, + otherwise will construct a Rainbow-style network. + name: str, used to create scope for network parameters. + activation_fn: function, passed to the layer constructors. + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleDQNNetwork.md new file mode 100644 index 00000000..5a51b3b7 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleDQNNetwork.md @@ -0,0 +1,27 @@ +description: Keras DQN network for Cartpole. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.CartpoleDQNNetwork + + + + + + + + + +Keras DQN network for Cartpole. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleFourierDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleFourierDQNNetwork.md new file mode 100644 index 00000000..e90abd64 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleFourierDQNNetwork.md @@ -0,0 +1,29 @@ +description: Keras network for fourier Cartpole. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.CartpoleFourierDQNNetwork + + + + + + + + + +Keras network for fourier Cartpole. + +Inherits From: [`FourierDQNNetwork`](../../../dopamine/discrete_domains/gym_lib/FourierDQNNetwork.md) + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleRainbowNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleRainbowNetwork.md new file mode 100644 index 00000000..4e0d5c7c --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/CartpoleRainbowNetwork.md @@ -0,0 +1,27 @@ +description: Keras Rainbow network for Cartpole. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.CartpoleRainbowNetwork + + + + + + + + + +Keras Rainbow network for Cartpole. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/FourierBasis.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/FourierBasis.md new file mode 100644 index 00000000..d4b34daf --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/FourierBasis.md @@ -0,0 +1,38 @@ +description: Fourier Basis linear function approximation. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.FourierBasis + + + + + + + + + +Fourier Basis linear function approximation. + + + +Requires the ranges for each dimension, and is thus able to use only sine or +cosine (and uses cosine). So, this has half the coefficients that a full +Fourier approximation would use. + +Many thanks to Will Dabney (wdabney@) for this implementation. + +#### From the paper: + + +G.D. Konidaris, S. Osentoski and P.S. Thomas. (2011) +Value Function Approximation in Reinforcement Learning using the Fourier Basis + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/FourierDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/FourierDQNNetwork.md new file mode 100644 index 00000000..b9c36584 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/FourierDQNNetwork.md @@ -0,0 +1,27 @@ +description: Keras model for DQN. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.FourierDQNNetwork + + + + + + + + + +Keras model for DQN. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/LunarLanderDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/LunarLanderDQNNetwork.md new file mode 100644 index 00000000..59328a95 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/LunarLanderDQNNetwork.md @@ -0,0 +1,27 @@ +description: Keras DQN network for LunarLander. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.LunarLanderDQNNetwork + + + + + + + + + +Keras DQN network for LunarLander. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/gym_lib/MountainCarDQNNetwork.md b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/MountainCarDQNNetwork.md new file mode 100644 index 00000000..31279830 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/gym_lib/MountainCarDQNNetwork.md @@ -0,0 +1,27 @@ +description: Keras DQN network for MountainCar. + +
+ + +
+ +# dopamine.discrete_domains.gym_lib.MountainCarDQNNetwork + + + + + + + + + +Keras DQN network for MountainCar. + + + + diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks.md new file mode 100644 index 00000000..36131f84 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks.md @@ -0,0 +1,48 @@ +description: Legacy (pre-Keras) network architectures. + +
+ + +
+ +# Module: dopamine.discrete_domains.legacy_networks + + + + + + + + + +Legacy (pre-Keras) network architectures. + + + +## Functions + +[`acrobot_dqn_network(...)`](../../dopamine/discrete_domains/legacy_networks/acrobot_dqn_network.md): Builds the deep network used to compute the agent's Q-values. + +[`acrobot_fourier_dqn_network(...)`](../../dopamine/discrete_domains/legacy_networks/acrobot_fourier_dqn_network.md): Builds the function approximator used to compute the agent's Q-values. + +[`acrobot_rainbow_network(...)`](../../dopamine/discrete_domains/legacy_networks/acrobot_rainbow_network.md): Build the deep network used to compute the agent's Q-value distributions. + +[`cartpole_dqn_network(...)`](../../dopamine/discrete_domains/legacy_networks/cartpole_dqn_network.md): Builds the deep network used to compute the agent's Q-values. + +[`cartpole_fourier_dqn_network(...)`](../../dopamine/discrete_domains/legacy_networks/cartpole_fourier_dqn_network.md): Builds the function approximator used to compute the agent's Q-values. + +[`cartpole_rainbow_network(...)`](../../dopamine/discrete_domains/legacy_networks/cartpole_rainbow_network.md): Build the deep network used to compute the agent's Q-value distributions. + +[`fourier_dqn_network(...)`](../../dopamine/discrete_domains/legacy_networks/fourier_dqn_network.md): Builds the function approximator used to compute the agent's Q-values. + +[`implicit_quantile_network(...)`](../../dopamine/discrete_domains/legacy_networks/implicit_quantile_network.md): The Implicit Quantile ConvNet. + +[`nature_dqn_network(...)`](../../dopamine/discrete_domains/legacy_networks/nature_dqn_network.md): The convolutional network used to compute the agent's Q-values. + +[`rainbow_network(...)`](../../dopamine/discrete_domains/legacy_networks/rainbow_network.md): The convolutional network used to compute agent's Q-value distributions. + diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_dqn_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_dqn_network.md new file mode 100644 index 00000000..9bebbb8b --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_dqn_network.md @@ -0,0 +1,83 @@ +description: Builds the deep network used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.acrobot_dqn_network + + + + + + + + + +Builds the deep network used to compute the agent's Q-values. + + +
+dopamine.discrete_domains.legacy_networks.acrobot_dqn_network(
+    num_actions, network_type, state
+)
+
+ + + + + +It rescales the input features to a range that yields improved performance. + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`network_type` + +namedtuple, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_fourier_dqn_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_fourier_dqn_network.md new file mode 100644 index 00000000..85ac4909 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_fourier_dqn_network.md @@ -0,0 +1,83 @@ +description: Builds the function approximator used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.acrobot_fourier_dqn_network + + + + + + + + + +Builds the function approximator used to compute the agent's Q-values. + + +
+dopamine.discrete_domains.legacy_networks.acrobot_fourier_dqn_network(
+    num_actions, network_type, state
+)
+
+ + + + + +It uses the Fourier basis features and a linear function approximator. + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`network_type` + +namedtuple, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_rainbow_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_rainbow_network.md new file mode 100644 index 00000000..c2f17c3b --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/acrobot_rainbow_network.md @@ -0,0 +1,96 @@ +description: Build the deep network used to compute the agent's Q-value distributions. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.acrobot_rainbow_network + + + + + + + + + +Build the deep network used to compute the agent's Q-value distributions. + + +
+dopamine.discrete_domains.legacy_networks.acrobot_rainbow_network(
+    num_actions, num_atoms, support, network_type, state
+)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`num_atoms` + +int, the number of buckets of the value function distribution. +
+`support` + +tf.linspace, the support of the Q-value distribution. +
+`network_type` + +`namedtuple`, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_dqn_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_dqn_network.md new file mode 100644 index 00000000..33b21b42 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_dqn_network.md @@ -0,0 +1,83 @@ +description: Builds the deep network used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.cartpole_dqn_network + + + + + + + + + +Builds the deep network used to compute the agent's Q-values. + + +
+dopamine.discrete_domains.legacy_networks.cartpole_dqn_network(
+    num_actions, network_type, state
+)
+
+ + + + + +It rescales the input features to a range that yields improved performance. + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`network_type` + +namedtuple, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_fourier_dqn_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_fourier_dqn_network.md new file mode 100644 index 00000000..362a1a6d --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_fourier_dqn_network.md @@ -0,0 +1,83 @@ +description: Builds the function approximator used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.cartpole_fourier_dqn_network + + + + + + + + + +Builds the function approximator used to compute the agent's Q-values. + + +
+dopamine.discrete_domains.legacy_networks.cartpole_fourier_dqn_network(
+    num_actions, network_type, state
+)
+
+ + + + + +It uses the Fourier basis features and a linear function approximator. + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`network_type` + +namedtuple, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_rainbow_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_rainbow_network.md new file mode 100644 index 00000000..8c3f0105 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/cartpole_rainbow_network.md @@ -0,0 +1,96 @@ +description: Build the deep network used to compute the agent's Q-value distributions. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.cartpole_rainbow_network + + + + + + + + + +Build the deep network used to compute the agent's Q-value distributions. + + +
+dopamine.discrete_domains.legacy_networks.cartpole_rainbow_network(
+    num_actions, num_atoms, support, network_type, state
+)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`num_atoms` + +int, the number of buckets of the value function distribution. +
+`support` + +tf.linspace, the support of the Q-value distribution. +
+`network_type` + +`namedtuple`, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/fourier_dqn_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/fourier_dqn_network.md new file mode 100644 index 00000000..4fb99136 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/fourier_dqn_network.md @@ -0,0 +1,94 @@ +description: Builds the function approximator used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.fourier_dqn_network + + + + + + + + + +Builds the function approximator used to compute the agent's Q-values. + + +
+dopamine.discrete_domains.legacy_networks.fourier_dqn_network(
+    min_vals, max_vals, num_actions, state, fourier_basis_order=3
+)
+
+ + + + + +It uses FourierBasis features and a linear layer. + + + + + + + + + + + + + + + + + + + + + + +

Args

+`min_vals` + +float, minimum attainable values (must be same shape as `state`). +
+`max_vals` + +float, maximum attainable values (must be same shape as `state`). +
+`num_actions` + +int, number of actions. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+`fourier_basis_order` + +int, order of the Fourier basis functions. +
+ + + + + + + + + + + +

Returns

+The Q-values for DQN-style agents or logits for Rainbow-style agents. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/implicit_quantile_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/implicit_quantile_network.md new file mode 100644 index 00000000..bea82173 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/implicit_quantile_network.md @@ -0,0 +1,96 @@ +description: The Implicit Quantile ConvNet. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.implicit_quantile_network + + + + + + + + + +The Implicit Quantile ConvNet. + + +
+dopamine.discrete_domains.legacy_networks.implicit_quantile_network(
+    num_actions, quantile_embedding_dim, network_type, state, num_quantiles
+)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`quantile_embedding_dim` + +int, embedding dimension for the quantile input. +
+`network_type` + +namedtuple, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+`num_quantiles` + +int, number of quantile inputs. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/nature_dqn_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/nature_dqn_network.md new file mode 100644 index 00000000..0874147c --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/nature_dqn_network.md @@ -0,0 +1,82 @@ +description: The convolutional network used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.nature_dqn_network + + + + + + + + + +The convolutional network used to compute the agent's Q-values. + + +
+dopamine.discrete_domains.legacy_networks.nature_dqn_network(
+    num_actions, network_type, state
+)
+
+ + + + + + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`network_type` + +namedtuple, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/rainbow_network.md b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/rainbow_network.md new file mode 100644 index 00000000..c5a6529e --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/legacy_networks/rainbow_network.md @@ -0,0 +1,96 @@ +description: The convolutional network used to compute agent's Q-value distributions. + +
+ + +
+ +# dopamine.discrete_domains.legacy_networks.rainbow_network + + + + + + + + + +The convolutional network used to compute agent's Q-value distributions. + + +
+dopamine.discrete_domains.legacy_networks.rainbow_network(
+    num_actions, num_atoms, support, network_type, state
+)
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +

Args

+`num_actions` + +int, number of actions. +
+`num_atoms` + +int, the number of buckets of the value function distribution. +
+`support` + +tf.linspace, the support of the Q-value distribution. +
+`network_type` + +namedtuple, collection of expected values to return. +
+`state` + +`tf.Tensor`, contains the agent's current state. +
+ + + + + + + + + + + + +

Returns

+`net` + +_network_type object containing the tensors output by the network. +
+ diff --git a/docs/api_docs/python/dopamine/discrete_domains/run_experiment/load_gin_configs.md b/docs/api_docs/python/dopamine/discrete_domains/run_experiment/load_gin_configs.md new file mode 100644 index 00000000..b0b7bb97 --- /dev/null +++ b/docs/api_docs/python/dopamine/discrete_domains/run_experiment/load_gin_configs.md @@ -0,0 +1,60 @@ +description: Loads gin configuration files. + +
+ + +
+ +# dopamine.discrete_domains.run_experiment.load_gin_configs + + + + + + + + + +Loads gin configuration files. + + +
+dopamine.discrete_domains.run_experiment.load_gin_configs(
+    gin_files, gin_bindings
+)
+
+ + + + + + + + + + + + + + + + + + +

Args

+`gin_files` + +list, of paths to the gin configuration files for this +experiment. +
+`gin_bindings` + +list, of gin parameter bindings to override the values in the +config files. +
+ diff --git a/docs/api_docs/python/dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md b/docs/api_docs/python/dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md new file mode 100644 index 00000000..006fdde7 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md @@ -0,0 +1,27 @@ +description: A JAX implementation of the DQN agent. + +
+ + +
+ +# dopamine.jax.agents.dqn.dqn_agent.JaxDQNAgent + + + + + + + + + +A JAX implementation of the DQN agent. + + + + diff --git a/docs/api_docs/python/dopamine/jax/agents/full_rainbow.md b/docs/api_docs/python/dopamine/jax/agents/full_rainbow.md new file mode 100644 index 00000000..d7917558 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/full_rainbow.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.jax.agents.full_rainbow + + + + + + + + + + + + + +## Modules + +[`full_rainbow_agent`](../../../dopamine/jax/agents/full_rainbow/full_rainbow_agent.md) module: Compact implementation of the full Rainbow agent in JAX. + diff --git a/docs/api_docs/python/dopamine/jax/agents/full_rainbow/full_rainbow_agent.md b/docs/api_docs/python/dopamine/jax/agents/full_rainbow/full_rainbow_agent.md new file mode 100644 index 00000000..e63e12d1 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/full_rainbow/full_rainbow_agent.md @@ -0,0 +1,41 @@ +description: Compact implementation of the full Rainbow agent in JAX. + +
+ + +
+ +# Module: dopamine.jax.agents.full_rainbow.full_rainbow_agent + + + + + + + + + +Compact implementation of the full Rainbow agent in JAX. + + +Specifically, we implement the following components from Rainbow: + + * n-step updates + * prioritized replay + * distributional RL + * double_dqn + * noisy + * dueling + +Details in "Rainbow: Combining Improvements in Deep Reinforcement Learning" by +Hessel et al. (2018). + +## Classes + +[`class JaxFullRainbowAgent`](../../../../dopamine/jax/agents/full_rainbow/full_rainbow_agent/JaxFullRainbowAgent.md): A compact implementation of the full Rainbow agent. + diff --git a/docs/api_docs/python/dopamine/jax/agents/full_rainbow/full_rainbow_agent/JaxFullRainbowAgent.md b/docs/api_docs/python/dopamine/jax/agents/full_rainbow/full_rainbow_agent/JaxFullRainbowAgent.md new file mode 100644 index 00000000..73824851 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/full_rainbow/full_rainbow_agent/JaxFullRainbowAgent.md @@ -0,0 +1,29 @@ +description: A compact implementation of the full Rainbow agent. + +
+ + +
+ +# dopamine.jax.agents.full_rainbow.full_rainbow_agent.JaxFullRainbowAgent + + + + + + + + + +A compact implementation of the full Rainbow agent. + +Inherits From: [`JaxDQNAgent`](../../../../../dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent/JaxImplicitQuantileAgent.md b/docs/api_docs/python/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent/JaxImplicitQuantileAgent.md new file mode 100644 index 00000000..f3ab9d2e --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent/JaxImplicitQuantileAgent.md @@ -0,0 +1,29 @@ +description: An extension of Rainbow to perform implicit quantile regression. + +
+ + +
+ +# dopamine.jax.agents.implicit_quantile.implicit_quantile_agent.JaxImplicitQuantileAgent + + + + + + + + + +An extension of Rainbow to perform implicit quantile regression. + +Inherits From: [`JaxDQNAgent`](../../../../../dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/agents/ppo.md b/docs/api_docs/python/dopamine/jax/agents/ppo.md new file mode 100644 index 00000000..1482d60e --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/ppo.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.jax.agents.ppo + + + + + + + + + + + + + +## Modules + +[`ppo_agent`](../../../dopamine/jax/agents/ppo/ppo_agent.md) module: Compact implementation of a PPO agent in JAX. + diff --git a/docs/api_docs/python/dopamine/jax/agents/ppo/ppo_agent.md b/docs/api_docs/python/dopamine/jax/agents/ppo/ppo_agent.md new file mode 100644 index 00000000..e0b79fc3 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/ppo/ppo_agent.md @@ -0,0 +1,34 @@ +description: Compact implementation of a PPO agent in JAX. + +
+ + +
+ +# Module: dopamine.jax.agents.ppo.ppo_agent + + + + + + + + + +Compact implementation of a PPO agent in JAX. + + +Based on agent described in + "Proximal Policy Optimization Algorithms" + by John Schulman et al. + https://arxiv.org/abs/1707.06347 + +## Classes + +[`class PPOAgent`](../../../../dopamine/jax/agents/ppo/ppo_agent/PPOAgent.md): A JAX implementation of the PPO agent. + diff --git a/docs/api_docs/python/dopamine/jax/agents/ppo/ppo_agent/PPOAgent.md b/docs/api_docs/python/dopamine/jax/agents/ppo/ppo_agent/PPOAgent.md new file mode 100644 index 00000000..f317aa0f --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/ppo/ppo_agent/PPOAgent.md @@ -0,0 +1,29 @@ +description: A JAX implementation of the PPO agent. + +
+ + +
+ +# dopamine.jax.agents.ppo.ppo_agent.PPOAgent + + + + + + + + + +A JAX implementation of the PPO agent. + +Inherits From: [`JaxDQNAgent`](../../../../../dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/agents/quantile/quantile_agent/JaxQuantileAgent.md b/docs/api_docs/python/dopamine/jax/agents/quantile/quantile_agent/JaxQuantileAgent.md new file mode 100644 index 00000000..702e8a17 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/quantile/quantile_agent/JaxQuantileAgent.md @@ -0,0 +1,29 @@ +description: An implementation of Quantile regression DQN agent. + +
+ + +
+ +# dopamine.jax.agents.quantile.quantile_agent.JaxQuantileAgent + + + + + + + + + +An implementation of Quantile regression DQN agent. + +Inherits From: [`JaxDQNAgent`](../../../../../dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/agents/rainbow/rainbow_agent/JaxRainbowAgent.md b/docs/api_docs/python/dopamine/jax/agents/rainbow/rainbow_agent/JaxRainbowAgent.md new file mode 100644 index 00000000..8fc00460 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/rainbow/rainbow_agent/JaxRainbowAgent.md @@ -0,0 +1,29 @@ +description: A compact implementation of a simplified Rainbow agent. + +
+ + +
+ +# dopamine.jax.agents.rainbow.rainbow_agent.JaxRainbowAgent + + + + + + + + + +A compact implementation of a simplified Rainbow agent. + +Inherits From: [`JaxDQNAgent`](../../../../../dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/agents/sac.md b/docs/api_docs/python/dopamine/jax/agents/sac.md new file mode 100644 index 00000000..f81b96aa --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/sac.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.jax.agents.sac + + + + + + + + + + + + + +## Modules + +[`sac_agent`](../../../dopamine/jax/agents/sac/sac_agent.md) module: Compact implementation of a Soft Actor-Critic agent in JAX. + diff --git a/docs/api_docs/python/dopamine/jax/agents/sac/sac_agent.md b/docs/api_docs/python/dopamine/jax/agents/sac/sac_agent.md new file mode 100644 index 00000000..8ff0f581 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/sac/sac_agent.md @@ -0,0 +1,34 @@ +description: Compact implementation of a Soft Actor-Critic agent in JAX. + +
+ + +
+ +# Module: dopamine.jax.agents.sac.sac_agent + + + + + + + + + +Compact implementation of a Soft Actor-Critic agent in JAX. + + +Based on agent described in + "Soft Actor-Critic Algorithms and Applications" + by Tuomas Haarnoja et al. + https://arxiv.org/abs/1812.05905 + +## Classes + +[`class SACAgent`](../../../../dopamine/jax/agents/sac/sac_agent/SACAgent.md): A JAX implementation of the SAC agent. + diff --git a/docs/api_docs/python/dopamine/jax/agents/sac/sac_agent/SACAgent.md b/docs/api_docs/python/dopamine/jax/agents/sac/sac_agent/SACAgent.md new file mode 100644 index 00000000..f952bc5a --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/agents/sac/sac_agent/SACAgent.md @@ -0,0 +1,29 @@ +description: A JAX implementation of the SAC agent. + +
+ + +
+ +# dopamine.jax.agents.sac.sac_agent.SACAgent + + + + + + + + + +A JAX implementation of the SAC agent. + +Inherits From: [`JaxDQNAgent`](../../../../../dopamine/jax/agents/dqn/dqn_agent/JaxDQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/checkpointers.md b/docs/api_docs/python/dopamine/jax/checkpointers.md new file mode 100644 index 00000000..b5874bc4 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/checkpointers.md @@ -0,0 +1,82 @@ +description: Checkpointer using Orbax and MessagePack. + +
+ + +
+ +# Module: dopamine.jax.checkpointers + + + + + + + + + +Checkpointer using Orbax and MessagePack. + + + +#### Usage: + +1) Implement `to_state_dict` and `from_state_dict` on your class. +2) Create an `orbax.CheckpointManager` and use the `CheckpointHandler`. + +To save: call `save` on your checkpoint manager passing the class instance. +To restore: call `restore` on your checkpoint manager passing the class + instance. + + + +#### Example: + + +```py +from typing import Dict, Any + +import numpy.typing as npt +from orbax import checkpoint as orbax +from dopamine.jax import checkpoint + +class MyClass: + def __init__(self, data: npt.NDArray): + self._data = data + + def to_state_dict(self) -> Dict[str, Any]: + return {'data': self._data} + + def from_state_dict(self, state_dict: Dict[str, Any]) -> None: + self._data = state_dict['data'] + +checkpoint_manager = orbax.CheckpointManager( + my_workdir, + checkpointers=checkpoint.Checkpointer(), +) + +instance = MyClass(np.array([1, 2, 3])) +checkpoint_manager.save(1, instance) +# Creates a copy of instance +restored_instance = checkpoint_manager.restore(1, instance) + +# If you don't want to create a copy: +instance.from_state_dict(checkpoint_manager.restore(1)) +``` + + +## Classes + +[`class CheckpointHandler`](../../dopamine/jax/checkpointers/CheckpointHandler.md): Checkpointable protocol checkpoint handler. + +[`class Checkpointable`](../../dopamine/jax/checkpointers/Checkpointable.md): Checkpointable protocol. Must implement to_state_dict, from_state_dict. + +## Functions + +[`Checkpointer(...)`](../../dopamine/jax/checkpointers/Checkpointer.md): partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords. + diff --git a/docs/api_docs/python/dopamine/jax/checkpointers/CheckpointHandler.md b/docs/api_docs/python/dopamine/jax/checkpointers/CheckpointHandler.md new file mode 100644 index 00000000..ae9f27ad --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/checkpointers/CheckpointHandler.md @@ -0,0 +1,27 @@ +description: Checkpointable protocol checkpoint handler. + +
+ + +
+ +# dopamine.jax.checkpointers.CheckpointHandler + + + + + + + + + +Checkpointable protocol checkpoint handler. + + + + diff --git a/docs/api_docs/python/dopamine/jax/checkpointers/Checkpointable.md b/docs/api_docs/python/dopamine/jax/checkpointers/Checkpointable.md new file mode 100644 index 00000000..84910ceb --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/checkpointers/Checkpointable.md @@ -0,0 +1,27 @@ +description: Checkpointable protocol. Must implement to_state_dict, from_state_dict. + +
+ + +
+ +# dopamine.jax.checkpointers.Checkpointable + + + + + + + + + +Checkpointable protocol. Must implement to_state_dict, from_state_dict. + + + + diff --git a/docs/api_docs/python/dopamine/jax/checkpointers/Checkpointer.md b/docs/api_docs/python/dopamine/jax/checkpointers/Checkpointer.md new file mode 100644 index 00000000..7565e03c --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/checkpointers/Checkpointer.md @@ -0,0 +1,33 @@ +description: partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords. + +
+ + +
+ +# dopamine.jax.checkpointers.Checkpointer + + + + + + + + + +partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords. + + +
+dopamine.jax.checkpointers.Checkpointer(
+    *,
+    multiprocessing_options=MultiprocessingOptions(primary_host=0, active_processes=None, barrier_sync_key_prefix=None),
+    file_options=FileOptions(path_permission_mode=None, data_governance_annotations=None),
+    checkpoint_metadata_store=None,
+    temporary_path_class=None
+)
+
+ + + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks.md b/docs/api_docs/python/dopamine/jax/continuous_networks.md new file mode 100644 index 00000000..7d4b5fa5 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks.md @@ -0,0 +1,52 @@ +description: Networks for continuous control agents. + +
+ + +
+ +# Module: dopamine.jax.continuous_networks + + + + + + + + + +Networks for continuous control agents. + + + +## Classes + +[`class ActorCriticNetwork`](../../dopamine/jax/continuous_networks/ActorCriticNetwork.md): Non-convolutional actor critic value and policy networks. + +[`class ActorCriticOutput`](../../dopamine/jax/continuous_networks/ActorCriticOutput.md): The output of the actor critic network. + +[`class ActorNetwork`](../../dopamine/jax/continuous_networks/ActorNetwork.md): A simple continuous actor network. + +[`class ActorOutput`](../../dopamine/jax/continuous_networks/ActorOutput.md): The output of a continuous actor. + +[`class CriticNetwork`](../../dopamine/jax/continuous_networks/CriticNetwork.md): A simple continuous critic network. + +[`class CriticOutput`](../../dopamine/jax/continuous_networks/CriticOutput.md): The output of a continuous critic. + +[`class PPOActorCriticNetwork`](../../dopamine/jax/continuous_networks/PPOActorCriticNetwork.md): Actor critic network for PPO. + +[`class PPOActorCriticOutput`](../../dopamine/jax/continuous_networks/PPOActorCriticOutput.md): The output of the ppo actor critic network. + +[`class PPOActorNetwork`](../../dopamine/jax/continuous_networks/PPOActorNetwork.md): Actor network for PPO. + +[`class PPOActorOutput`](../../dopamine/jax/continuous_networks/PPOActorOutput.md): The output of a continuous ppo actor. + +[`class PPOCriticNetwork`](../../dopamine/jax/continuous_networks/PPOCriticNetwork.md): Critic network for PPO. + +[`class PPOCriticOutput`](../../dopamine/jax/continuous_networks/PPOCriticOutput.md): The output of a continuous ppo critic. + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/ActorCriticNetwork.md b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorCriticNetwork.md new file mode 100644 index 00000000..fee4cd71 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorCriticNetwork.md @@ -0,0 +1,95 @@ +description: Non-convolutional actor critic value and policy networks. + +
+ + +
+ +# dopamine.jax.continuous_networks.ActorCriticNetwork + + + + + + + + + +Non-convolutional actor critic value and policy networks. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`action_shape` + +Dataclass field +
+`num_layers` + +Dataclass field +
+`hidden_units` + +Dataclass field +
+`action_limits` + +Dataclass field +
+`activation` + +Dataclass field +
+`kernel_initializer` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/ActorCriticOutput.md b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorCriticOutput.md new file mode 100644 index 00000000..6e668426 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorCriticOutput.md @@ -0,0 +1,27 @@ +description: The output of the actor critic network. + +
+ + +
+ +# dopamine.jax.continuous_networks.ActorCriticOutput + + + + + + + + + +The output of the actor critic network. + + + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/ActorNetwork.md b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorNetwork.md new file mode 100644 index 00000000..41098259 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorNetwork.md @@ -0,0 +1,95 @@ +description: A simple continuous actor network. + +
+ + +
+ +# dopamine.jax.continuous_networks.ActorNetwork + + + + + + + + + +A simple continuous actor network. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`action_shape` + +Dataclass field +
+`num_layers` + +Dataclass field +
+`hidden_units` + +Dataclass field +
+`action_limits` + +Dataclass field +
+`activation` + +Dataclass field +
+`kernel_initializer` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/ActorOutput.md b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorOutput.md new file mode 100644 index 00000000..32ffd85e --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/ActorOutput.md @@ -0,0 +1,27 @@ +description: The output of a continuous actor. + +
+ + +
+ +# dopamine.jax.continuous_networks.ActorOutput + + + + + + + + + +The output of a continuous actor. + + + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/CriticNetwork.md b/docs/api_docs/python/dopamine/jax/continuous_networks/CriticNetwork.md new file mode 100644 index 00000000..48cab907 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/CriticNetwork.md @@ -0,0 +1,81 @@ +description: A simple continuous critic network. + +
+ + +
+ +# dopamine.jax.continuous_networks.CriticNetwork + + + + + + + + + +A simple continuous critic network. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_layers` + +Dataclass field +
+`hidden_units` + +Dataclass field +
+`activation` + +Dataclass field +
+`kernel_initializer` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/CriticOutput.md b/docs/api_docs/python/dopamine/jax/continuous_networks/CriticOutput.md new file mode 100644 index 00000000..9b7e5426 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/CriticOutput.md @@ -0,0 +1,27 @@ +description: The output of a continuous critic. + +
+ + +
+ +# dopamine.jax.continuous_networks.CriticOutput + + + + + + + + + +The output of a continuous critic. + + + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorCriticNetwork.md b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorCriticNetwork.md new file mode 100644 index 00000000..e9e334fb --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorCriticNetwork.md @@ -0,0 +1,88 @@ +description: Actor critic network for PPO. + +
+ + +
+ +# dopamine.jax.continuous_networks.PPOActorCriticNetwork + + + + + + + + + +Actor critic network for PPO. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`action_shape` + +shape of the action. +
+`num_layers` + +number of layers in the networks. +
+`hidden_units` + +number of hidden units in the networks. +
+`activation` + +activation function to use in the networks. +
+`action_limits` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorCriticOutput.md b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorCriticOutput.md new file mode 100644 index 00000000..850cf8f0 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorCriticOutput.md @@ -0,0 +1,27 @@ +description: The output of the ppo actor critic network. + +
+ + +
+ +# dopamine.jax.continuous_networks.PPOActorCriticOutput + + + + + + + + + +The output of the ppo actor critic network. + + + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorNetwork.md b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorNetwork.md new file mode 100644 index 00000000..e5726ac4 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorNetwork.md @@ -0,0 +1,88 @@ +description: Actor network for PPO. + +
+ + +
+ +# dopamine.jax.continuous_networks.PPOActorNetwork + + + + + + + + + +Actor network for PPO. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`action_shape` + +shape of the action. +
+`num_layers` + +number of layers in the network. +
+`hidden_units` + +number of hidden units in the network. +
+`activation` + +activation function to use in the network. +
+`action_limits` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorOutput.md b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorOutput.md new file mode 100644 index 00000000..93dedd98 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOActorOutput.md @@ -0,0 +1,27 @@ +description: The output of a continuous ppo actor. + +
+ + +
+ +# dopamine.jax.continuous_networks.PPOActorOutput + + + + + + + + + +The output of a continuous ppo actor. + + + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/PPOCriticNetwork.md b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOCriticNetwork.md new file mode 100644 index 00000000..d28842c7 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOCriticNetwork.md @@ -0,0 +1,74 @@ +description: Critic network for PPO. + +
+ + +
+ +# dopamine.jax.continuous_networks.PPOCriticNetwork + + + + + + + + + +Critic network for PPO. + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_layers` + +number of layers in the network. +
+`hidden_units` + +number of hidden units in the network. +
+`activation` + +activation function to use in the network. +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/continuous_networks/PPOCriticOutput.md b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOCriticOutput.md new file mode 100644 index 00000000..1a1ae1d9 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/continuous_networks/PPOCriticOutput.md @@ -0,0 +1,27 @@ +description: The output of a continuous ppo critic. + +
+ + +
+ +# dopamine.jax.continuous_networks.PPOCriticOutput + + + + + + + + + +The output of a continuous ppo critic. + + + + diff --git a/docs/api_docs/python/dopamine/jax/losses.md b/docs/api_docs/python/dopamine/jax/losses.md new file mode 100644 index 00000000..16823c20 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/losses.md @@ -0,0 +1,34 @@ +description: Various losses used by the Dopamine JAX agents. + +
+ + +
+ +# Module: dopamine.jax.losses + + + + + + + + + +Various losses used by the Dopamine JAX agents. + + + +## Functions + +[`huber_loss(...)`](../../dopamine/jax/losses/huber_loss.md): Implementation of the Huber loss with threshold delta. + +[`mse_loss(...)`](../../dopamine/jax/losses/mse_loss.md): Implementation of the mean squared error loss. + +[`softmax_cross_entropy_loss_with_logits(...)`](../../dopamine/jax/losses/softmax_cross_entropy_loss_with_logits.md): Implementation of the softmax cross entropy loss. + diff --git a/docs/api_docs/python/dopamine/jax/losses/huber_loss.md b/docs/api_docs/python/dopamine/jax/losses/huber_loss.md new file mode 100644 index 00000000..8947518e --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/losses/huber_loss.md @@ -0,0 +1,82 @@ +description: Implementation of the Huber loss with threshold delta. + +
+ + +
+ +# dopamine.jax.losses.huber_loss + + + + + + + + + +Implementation of the Huber loss with threshold delta. + + +
+dopamine.jax.losses.huber_loss(
+    targets: jnp.ndarray, predictions: jnp.ndarray, delta: float = 1.0
+) -> jnp.ndarray
+
+ + + + + +Let `x = |targets - predictions|`, the Huber loss is defined as: +`0.5 * x^2` if `x <= delta` +`0.5 * delta^2 + delta * (x - delta)` otherwise. + + + + + + + + + + + + + + + + +

Args

+`targets` + +Target values. +
+`predictions` + +Prediction values. +
+`delta` + +Threshold. +
+ + + + + + + + + + + +

Returns

+Huber loss. +
+ diff --git a/docs/api_docs/python/dopamine/jax/losses/mse_loss.md b/docs/api_docs/python/dopamine/jax/losses/mse_loss.md new file mode 100644 index 00000000..8c36cc06 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/losses/mse_loss.md @@ -0,0 +1,34 @@ +description: Implementation of the mean squared error loss. + +
+ + +
+ +# dopamine.jax.losses.mse_loss + + + + + + + + + +Implementation of the mean squared error loss. + + +
+dopamine.jax.losses.mse_loss(
+    targets: jnp.ndarray, predictions: jnp.ndarray
+) -> jnp.ndarray
+
+ + + + diff --git a/docs/api_docs/python/dopamine/jax/losses/softmax_cross_entropy_loss_with_logits.md b/docs/api_docs/python/dopamine/jax/losses/softmax_cross_entropy_loss_with_logits.md new file mode 100644 index 00000000..dcf8c258 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/losses/softmax_cross_entropy_loss_with_logits.md @@ -0,0 +1,34 @@ +description: Implementation of the softmax cross entropy loss. + +
+ + +
+ +# dopamine.jax.losses.softmax_cross_entropy_loss_with_logits + + + + + + + + + +Implementation of the softmax cross entropy loss. + + +
+dopamine.jax.losses.softmax_cross_entropy_loss_with_logits(
+    labels: jnp.ndarray, logits: jnp.ndarray
+) -> jnp.ndarray
+
+ + + + diff --git a/docs/api_docs/python/dopamine/jax/networks/ClassicControlDQNNetwork.md b/docs/api_docs/python/dopamine/jax/networks/ClassicControlDQNNetwork.md new file mode 100644 index 00000000..0dfc3001 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/ClassicControlDQNNetwork.md @@ -0,0 +1,95 @@ +description: Jax DQN network for classic control environments. + +
+ + +
+ +# dopamine.jax.networks.ClassicControlDQNNetwork + + + + + + + + + +Jax DQN network for classic control environments. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`num_layers` + +Dataclass field +
+`hidden_units` + +Dataclass field +
+`min_vals` + +Dataclass field +
+`max_vals` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/ClassicControlRainbowNetwork.md b/docs/api_docs/python/dopamine/jax/networks/ClassicControlRainbowNetwork.md new file mode 100644 index 00000000..cbb986ab --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/ClassicControlRainbowNetwork.md @@ -0,0 +1,102 @@ +description: Jax Rainbow network for classic control environments. + +
+ + +
+ +# dopamine.jax.networks.ClassicControlRainbowNetwork + + + + + + + + + +Jax Rainbow network for classic control environments. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`num_atoms` + +Dataclass field +
+`num_layers` + +Dataclass field +
+`hidden_units` + +Dataclass field +
+`min_vals` + +Dataclass field +
+`max_vals` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/FourierBasis.md b/docs/api_docs/python/dopamine/jax/networks/FourierBasis.md new file mode 100644 index 00000000..79557826 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/FourierBasis.md @@ -0,0 +1,38 @@ +description: Fourier Basis linear function approximation. + +
+ + +
+ +# dopamine.jax.networks.FourierBasis + + + + + + + + + +Fourier Basis linear function approximation. + + + +Requires the ranges for each dimension, and is thus able to use only sine or +cosine (and uses cosine). So, this has half the coefficients that a full +Fourier approximation would use. + +Adapted from Will Dabney's (wdabney@) TF implementation for JAX. + +#### From the paper: + + +G.D. Konidaris, S. Osentoski and P.S. Thomas. (2011) +Value Function Approximation in Reinforcement Learning using the Fourier Basis + diff --git a/docs/api_docs/python/dopamine/jax/networks/FullRainbowNetwork.md b/docs/api_docs/python/dopamine/jax/networks/FullRainbowNetwork.md new file mode 100644 index 00000000..eef92e22 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/FullRainbowNetwork.md @@ -0,0 +1,95 @@ +description: Jax Rainbow network for Full Rainbow. + +
+ + +
+ +# dopamine.jax.networks.FullRainbowNetwork + + + + + + + + + +Jax Rainbow network for Full Rainbow. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +int, number of actions the agent can take at any state. +
+`num_atoms` + +int, the number of buckets of the value function distribution. +
+`noisy` + +bool, Whether to use noisy networks. +
+`dueling` + +bool, Whether to use dueling network architecture. +
+`distributional` + +bool, whether to use distributional RL. +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/ImpalaDQNNetwork.md b/docs/api_docs/python/dopamine/jax/networks/ImpalaDQNNetwork.md new file mode 100644 index 00000000..9d6c9e9a --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/ImpalaDQNNetwork.md @@ -0,0 +1,74 @@ +description: The convolutional network used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.jax.networks.ImpalaDQNNetwork + + + + + + + + + +The convolutional network used to compute the agent's Q-values. + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`nn_scale` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/ImpalaEncoder.md b/docs/api_docs/python/dopamine/jax/networks/ImpalaEncoder.md new file mode 100644 index 00000000..866bddbc --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/ImpalaEncoder.md @@ -0,0 +1,74 @@ +description: Impala Network which also outputs penultimate representation layers. + +
+ + +
+ +# dopamine.jax.networks.ImpalaEncoder + + + + + + + + + +Impala Network which also outputs penultimate representation layers. + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`nn_scale` + +Dataclass field +
+`stack_sizes` + +Dataclass field +
+`num_blocks` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/ImplicitQuantileNetwork.md b/docs/api_docs/python/dopamine/jax/networks/ImplicitQuantileNetwork.md new file mode 100644 index 00000000..70e7bc25 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/ImplicitQuantileNetwork.md @@ -0,0 +1,74 @@ +description: The Implicit Quantile Network (Dabney et al., 2018).. + +
+ + +
+ +# dopamine.jax.networks.ImplicitQuantileNetwork + + + + + + + + + +The Implicit Quantile Network (Dabney et al., 2018).. + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`quantile_embedding_dim` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/JaxFourierDQNNetwork.md b/docs/api_docs/python/dopamine/jax/networks/JaxFourierDQNNetwork.md new file mode 100644 index 00000000..fec6ab5c --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/JaxFourierDQNNetwork.md @@ -0,0 +1,81 @@ +description: Fourier-basis for DQN-like agents. + +
+ + +
+ +# dopamine.jax.networks.JaxFourierDQNNetwork + + + + + + + + + +Fourier-basis for DQN-like agents. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`min_vals` + +Dataclass field +
+`max_vals` + +Dataclass field +
+`fourier_basis_order` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/NatureDQNNetwork.md b/docs/api_docs/python/dopamine/jax/networks/NatureDQNNetwork.md new file mode 100644 index 00000000..ad06fbab --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/NatureDQNNetwork.md @@ -0,0 +1,67 @@ +description: The convolutional network used to compute the agent's Q-values. + +
+ + +
+ +# dopamine.jax.networks.NatureDQNNetwork + + + + + + + + + +The convolutional network used to compute the agent's Q-values. + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/NoisyNetwork.md b/docs/api_docs/python/dopamine/jax/networks/NoisyNetwork.md new file mode 100644 index 00000000..18f2a2a7 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/NoisyNetwork.md @@ -0,0 +1,67 @@ +description: Noisy Network from Fortunato et al. (2018). + +
+ + +
+ +# dopamine.jax.networks.NoisyNetwork + + + + + + + + + +Noisy Network from Fortunato et al. (2018). + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`rng_key` + +jax.interpreters.xla.DeviceArray, key for JAX RNG. +
+`eval_mode` + +bool, whether to turn off noise during evaluation. +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/PPOActorNetwork.md b/docs/api_docs/python/dopamine/jax/networks/PPOActorNetwork.md new file mode 100644 index 00000000..57a90f1c --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/PPOActorNetwork.md @@ -0,0 +1,60 @@ +description: Actor backbone for PPO. + +
+ + +
+ +# dopamine.jax.networks.PPOActorNetwork + + + + + + + + + +Actor backbone for PPO. + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/PPOCriticNetwork.md b/docs/api_docs/python/dopamine/jax/networks/PPOCriticNetwork.md new file mode 100644 index 00000000..e06f6c7a --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/PPOCriticNetwork.md @@ -0,0 +1,53 @@ +description: Critic backbone for PPO. + +
+ + +
+ +# dopamine.jax.networks.PPOCriticNetwork + + + + + + + + + +Critic backbone for PPO. + + + + + + + + + + + + + + + + + + +

Attributes

+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/PPODiscreteActorCriticNetwork.md b/docs/api_docs/python/dopamine/jax/networks/PPODiscreteActorCriticNetwork.md new file mode 100644 index 00000000..85099e8b --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/PPODiscreteActorCriticNetwork.md @@ -0,0 +1,67 @@ +description: Convolutional actor critic value and policy networks. + +
+ + +
+ +# dopamine.jax.networks.PPODiscreteActorCriticNetwork + + + + + + + + + +Convolutional actor critic value and policy networks. + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`action_shape` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/PPOSharedNetwork.md b/docs/api_docs/python/dopamine/jax/networks/PPOSharedNetwork.md new file mode 100644 index 00000000..865264b0 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/PPOSharedNetwork.md @@ -0,0 +1,60 @@ +description: Shared network for PPO actor and critic. + +
+ + +
+ +# dopamine.jax.networks.PPOSharedNetwork + + + + + + + + + +Shared network for PPO actor and critic. + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/QuantileNetwork.md b/docs/api_docs/python/dopamine/jax/networks/QuantileNetwork.md new file mode 100644 index 00000000..7e1b12b8 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/QuantileNetwork.md @@ -0,0 +1,74 @@ +description: Convolutional network used to compute the agent's return quantiles. + +
+ + +
+ +# dopamine.jax.networks.QuantileNetwork + + + + + + + + + +Convolutional network used to compute the agent's return quantiles. + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`num_atoms` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/RainbowNetwork.md b/docs/api_docs/python/dopamine/jax/networks/RainbowNetwork.md new file mode 100644 index 00000000..78407759 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/RainbowNetwork.md @@ -0,0 +1,74 @@ +description: Convolutional network used to compute the agent's return distributions. + +
+ + +
+ +# dopamine.jax.networks.RainbowNetwork + + + + + + + + + +Convolutional network used to compute the agent's return distributions. + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_actions` + +Dataclass field +
+`num_atoms` + +Dataclass field +
+`inputs_preprocessed` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/Stack.md b/docs/api_docs/python/dopamine/jax/networks/Stack.md new file mode 100644 index 00000000..1e1ec198 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/Stack.md @@ -0,0 +1,74 @@ +description: Stack of pooling and convolutional blocks with residual connections. + +
+ + +
+ +# dopamine.jax.networks.Stack + + + + + + + + + +Stack of pooling and convolutional blocks with residual connections. + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`num_ch` + +Dataclass field +
+`num_blocks` + +Dataclass field +
+`use_max_pooling` + +Dataclass field +
+`parent` + +Dataclass field +
+`name` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/networks/feature_layer.md b/docs/api_docs/python/dopamine/jax/networks/feature_layer.md new file mode 100644 index 00000000..362247ef --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/feature_layer.md @@ -0,0 +1,34 @@ +description: Network feature layer depending on whether noisy_nets are used on or not. + +
+ + +
+ +# dopamine.jax.networks.feature_layer + + + + + + + + + +Network feature layer depending on whether noisy_nets are used on or not. + + +
+dopamine.jax.networks.feature_layer(
+    key, noisy, eval_mode=False
+)
+
+ + + + diff --git a/docs/api_docs/python/dopamine/jax/networks/preprocess_atari_inputs.md b/docs/api_docs/python/dopamine/jax/networks/preprocess_atari_inputs.md new file mode 100644 index 00000000..fe1aa5da --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/networks/preprocess_atari_inputs.md @@ -0,0 +1,34 @@ +description: Input normalization for Atari 2600 input frames. + +
+ + +
+ +# dopamine.jax.networks.preprocess_atari_inputs + + + + + + + + + +Input normalization for Atari 2600 input frames. + + +
+dopamine.jax.networks.preprocess_atari_inputs(
+    x
+)
+
+ + + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory.md b/docs/api_docs/python/dopamine/jax/replay_memory.md new file mode 100644 index 00000000..4e874d1c --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory.md @@ -0,0 +1,36 @@ +
+ + +
+ +# Module: dopamine.jax.replay_memory + + + + + + + + + + + + + +## Modules + +[`accumulator`](../../dopamine/jax/replay_memory/accumulator.md) module: An accumulator used by the Jax replay buffer. + +[`elements`](../../dopamine/jax/replay_memory/elements.md) module: Various containers used by the replay buffer code. + +[`replay_buffer`](../../dopamine/jax/replay_memory/replay_buffer.md) module: Simpler implementation of the standard DQN replay memory. + +[`samplers`](../../dopamine/jax/replay_memory/samplers.md) module: Sampling distributions. + +[`sum_tree`](../../dopamine/jax/replay_memory/sum_tree.md) module: Sum Tree. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/accumulator.md b/docs/api_docs/python/dopamine/jax/replay_memory/accumulator.md new file mode 100644 index 00000000..1d6b3248 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/accumulator.md @@ -0,0 +1,32 @@ +description: An accumulator used by the Jax replay buffer. + +
+ + +
+ +# Module: dopamine.jax.replay_memory.accumulator + + + + + + + + + +An accumulator used by the Jax replay buffer. + + + +## Classes + +[`class Accumulator`](../../../dopamine/jax/replay_memory/accumulator/Accumulator.md): Checkpointable protocol. Must implement to_state_dict, from_state_dict. + +[`class TransitionAccumulator`](../../../dopamine/jax/replay_memory/accumulator/TransitionAccumulator.md): A transition accumulator used for preparing elements for replay. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/accumulator/Accumulator.md b/docs/api_docs/python/dopamine/jax/replay_memory/accumulator/Accumulator.md new file mode 100644 index 00000000..5bf95ac9 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/accumulator/Accumulator.md @@ -0,0 +1,29 @@ +description: Checkpointable protocol. Must implement to_state_dict, from_state_dict. + +
+ + +
+ +# dopamine.jax.replay_memory.accumulator.Accumulator + + + + + + + + + +Checkpointable protocol. Must implement to_state_dict, from_state_dict. + +Inherits From: [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/accumulator/TransitionAccumulator.md b/docs/api_docs/python/dopamine/jax/replay_memory/accumulator/TransitionAccumulator.md new file mode 100644 index 00000000..e5efaf2e --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/accumulator/TransitionAccumulator.md @@ -0,0 +1,34 @@ +description: A transition accumulator used for preparing elements for replay. + +
+ + +
+ +# dopamine.jax.replay_memory.accumulator.TransitionAccumulator + + + + + + + + + +A transition accumulator used for preparing elements for replay. + +Inherits From: [`Accumulator`](../../../../dopamine/jax/replay_memory/accumulator/Accumulator.md), [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + +This class will consume raw transitions and prepare them for storing in the +replay buffer. Specifically, it will only return validly stacked frames, and +will only return valid n-step returns. +This enables us to guarantee that the replay buffer only contains valid +elements to sample. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/elements.md b/docs/api_docs/python/dopamine/jax/replay_memory/elements.md new file mode 100644 index 00000000..f7b24712 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/elements.md @@ -0,0 +1,40 @@ +description: Various containers used by the replay buffer code. + +
+ + +
+ +# Module: dopamine.jax.replay_memory.elements + + + + + + + + + +Various containers used by the replay buffer code. + + + +## Classes + +[`class ReplayElement`](../../../dopamine/jax/replay_memory/elements/ReplayElement.md): A single replay transition element supporting compression. + +[`class ReplayElementProtocol`](../../../dopamine/jax/replay_memory/elements/ReplayElementProtocol.md): Base class for protocol classes. + +[`class TransitionElement`](../../../dopamine/jax/replay_memory/elements/TransitionElement.md): TransitionElement(observation, action, reward, is_terminal, episode_end) + +## Functions + +[`compress(...)`](../../../dopamine/jax/replay_memory/elements/compress.md): Compress a numpy array using snappy. + +[`uncompress(...)`](../../../dopamine/jax/replay_memory/elements/uncompress.md): Uncompress a numpy array that has been compressed via `compress`. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/elements/ReplayElement.md b/docs/api_docs/python/dopamine/jax/replay_memory/elements/ReplayElement.md new file mode 100644 index 00000000..979357aa --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/elements/ReplayElement.md @@ -0,0 +1,83 @@ +description: A single replay transition element supporting compression. + +
+ + +
+ +# dopamine.jax.replay_memory.elements.ReplayElement + + + + + + + + + +A single replay transition element supporting compression. + +Inherits From: [`ReplayElementProtocol`](../../../../dopamine/jax/replay_memory/elements/ReplayElementProtocol.md) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`state` + +Dataclass field +
+`action` + +Dataclass field +
+`reward` + +Dataclass field +
+`next_state` + +Dataclass field +
+`is_terminal` + +Dataclass field +
+`episode_end` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/elements/ReplayElementProtocol.md b/docs/api_docs/python/dopamine/jax/replay_memory/elements/ReplayElementProtocol.md new file mode 100644 index 00000000..f7ef34fd --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/elements/ReplayElementProtocol.md @@ -0,0 +1,55 @@ +description: Base class for protocol classes. + +
+ + +
+ +# dopamine.jax.replay_memory.elements.ReplayElementProtocol + + + + + + + + + +Base class for protocol classes. + + + +Protocol classes are defined as:: + + class Proto(Protocol): + def meth(self) -> int: + ... + +Such classes are primarily used with static type checkers that recognize +structural subtyping (static duck-typing). + +For example:: + + class C: + def meth(self) -> int: + return 0 + + def func(x: Proto) -> int: + return x.meth() + + func(C()) # Passes static type check + +See PEP 544 for details. Protocol classes decorated with +@typing.runtime_checkable act as simple-minded runtime protocols that check +only the presence of given attributes, ignoring their type signatures. +Protocol classes can be generic, they are defined as:: + + class GenProto(Protocol[T]): + def meth(self) -> T: + ... + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/elements/TransitionElement.md b/docs/api_docs/python/dopamine/jax/replay_memory/elements/TransitionElement.md new file mode 100644 index 00000000..473e8354 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/elements/TransitionElement.md @@ -0,0 +1,27 @@ +description: TransitionElement(observation, action, reward, is_terminal, episode_end) + +
+ + +
+ +# dopamine.jax.replay_memory.elements.TransitionElement + + + + + + + + + +TransitionElement(observation, action, reward, is_terminal, episode_end) + + + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/elements/compress.md b/docs/api_docs/python/dopamine/jax/replay_memory/elements/compress.md new file mode 100644 index 00000000..646f355a --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/elements/compress.md @@ -0,0 +1,68 @@ +description: Compress a numpy array using snappy. + +
+ + +
+ +# dopamine.jax.replay_memory.elements.compress + + + + + + + + + +Compress a numpy array using snappy. + + +
+dopamine.jax.replay_memory.elements.compress(
+    buffer: npt.NDArray
+) -> npt.NDArray
+
+ + + + + + + + + + + + + + + +

Args

+`buffer` + +npt.NDArray, buffer to compress. +
+ + + + + + + + + + + +

Returns

+Numpy structured array consisting of the following fields: +data: compressed data in bytes +shape: the shape of the uncompressed array +dtype: a string representation of the dtype +
+ diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/elements/uncompress.md b/docs/api_docs/python/dopamine/jax/replay_memory/elements/uncompress.md new file mode 100644 index 00000000..b0d005b4 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/elements/uncompress.md @@ -0,0 +1,65 @@ +description: Uncompress a numpy array that has been compressed via compress. + +
+ + +
+ +# dopamine.jax.replay_memory.elements.uncompress + + + + + + + + + +Uncompress a numpy array that has been compressed via `compress`. + + +
+dopamine.jax.replay_memory.elements.uncompress(
+    compressed: npt.NDArray
+) -> npt.NDArray
+
+ + + + + + + + + + + + + + + +

Args

+`compressed` + +npt.NDArray, numpy structured array with data, shape, and dtype. +
+ + + + + + + + + + + +

Returns

+Uncompressed npt.NDArray +
+ diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/replay_buffer.md b/docs/api_docs/python/dopamine/jax/replay_memory/replay_buffer.md new file mode 100644 index 00000000..8e0f5af1 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/replay_buffer.md @@ -0,0 +1,30 @@ +description: Simpler implementation of the standard DQN replay memory. + +
+ + +
+ +# Module: dopamine.jax.replay_memory.replay_buffer + + + + + + + + + +Simpler implementation of the standard DQN replay memory. + + + +## Classes + +[`class ReplayBuffer`](../../../dopamine/jax/replay_memory/replay_buffer/ReplayBuffer.md): A Jax re-implementation of the Dopamine Replay Buffer. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/replay_buffer/ReplayBuffer.md b/docs/api_docs/python/dopamine/jax/replay_memory/replay_buffer/ReplayBuffer.md new file mode 100644 index 00000000..35bfcfbe --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/replay_buffer/ReplayBuffer.md @@ -0,0 +1,51 @@ +description: A Jax re-implementation of the Dopamine Replay Buffer. + +
+ + +
+ +# dopamine.jax.replay_memory.replay_buffer.ReplayBuffer + + + + + + + + + +A Jax re-implementation of the Dopamine Replay Buffer. + +Inherits From: [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + +Stores transitions, state, action, reward, next_state, terminal (and any +extra contents specified) in a circular buffer and provides a uniform +sampling mechanism. + +The main changes from the original Dopamine implementation are: +- The original Dopamine replay buffer stored the raw observations and stacked + them upon sampling. Although space efficient, this is time inefficient. We + use the same compression mechanism used by dqn_zoo to store only stacked + states in a space efficient manner. +- Similarly, n-step transitions were computed at sample time. We only store + n-step returns in the buffer by using an Accumulator (which is also used for + stacking). +- The above two points allow us to sample uniformly from the FIFO queue, + without needing to determine invalid ranges (as in the original replay + buffer). The computation of invalid ranges was inefficient, as it required + verifying each sampled index, and potentially resampling them if they fell + in an invalid range. This computation scales linearly with the batch size, + and is thus quite inefficient. +- The original Dopamine replay buffer maintained a static array and performed + modulo arithmetic when adding/sampling. This is unnecessarily complicated + and we can achieve the same result by maintaining a FIFO queue (using an + OrderedDict structure) containing only valid transitions. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/samplers.md b/docs/api_docs/python/dopamine/jax/replay_memory/samplers.md new file mode 100644 index 00000000..b31ee593 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/samplers.md @@ -0,0 +1,40 @@ +description: Sampling distributions. + +
+ + +
+ +# Module: dopamine.jax.replay_memory.samplers + + + + + + + + + +Sampling distributions. + + + +## Classes + +[`class PrioritizedSampleMetadata`](../../../dopamine/jax/replay_memory/samplers/PrioritizedSampleMetadata.md) + +[`class PrioritizedSamplingDistribution`](../../../dopamine/jax/replay_memory/samplers/PrioritizedSamplingDistribution.md): A prioritized sampling distribution. + +[`class SampleMetadata`](../../../dopamine/jax/replay_memory/samplers/SampleMetadata.md) + +[`class SamplingDistribution`](../../../dopamine/jax/replay_memory/samplers/SamplingDistribution.md): A sampling distribution. + +[`class SequentialSamplingDistribution`](../../../dopamine/jax/replay_memory/samplers/SequentialSamplingDistribution.md): A sequential sampling distribution. + +[`class UniformSamplingDistribution`](../../../dopamine/jax/replay_memory/samplers/UniformSamplingDistribution.md): A uniform sampling distribution. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/samplers/PrioritizedSampleMetadata.md b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/PrioritizedSampleMetadata.md new file mode 100644 index 00000000..ffe37bf4 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/PrioritizedSampleMetadata.md @@ -0,0 +1,53 @@ +
+ + +
+ +# dopamine.jax.replay_memory.samplers.PrioritizedSampleMetadata + + + + + + + + + + + +Inherits From: [`SampleMetadata`](../../../../dopamine/jax/replay_memory/samplers/SampleMetadata.md) + + + + + + + + + + + + + + + + + + +

Attributes

+`keys` + +Dataclass field +
+`probabilities` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/samplers/PrioritizedSamplingDistribution.md b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/PrioritizedSamplingDistribution.md new file mode 100644 index 00000000..d178eacb --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/PrioritizedSamplingDistribution.md @@ -0,0 +1,29 @@ +description: A prioritized sampling distribution. + +
+ + +
+ +# dopamine.jax.replay_memory.samplers.PrioritizedSamplingDistribution + + + + + + + + + +A prioritized sampling distribution. + +Inherits From: [`UniformSamplingDistribution`](../../../../dopamine/jax/replay_memory/samplers/UniformSamplingDistribution.md), [`SamplingDistribution`](../../../../dopamine/jax/replay_memory/samplers/SamplingDistribution.md), [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SampleMetadata.md b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SampleMetadata.md new file mode 100644 index 00000000..e2739293 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SampleMetadata.md @@ -0,0 +1,44 @@ +
+ + +
+ +# dopamine.jax.replay_memory.samplers.SampleMetadata + + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`keys` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SamplingDistribution.md b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SamplingDistribution.md new file mode 100644 index 00000000..1f9baf07 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SamplingDistribution.md @@ -0,0 +1,29 @@ +description: A sampling distribution. + +
+ + +
+ +# dopamine.jax.replay_memory.samplers.SamplingDistribution + + + + + + + + + +A sampling distribution. + +Inherits From: [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SequentialSamplingDistribution.md b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SequentialSamplingDistribution.md new file mode 100644 index 00000000..572a6348 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/SequentialSamplingDistribution.md @@ -0,0 +1,29 @@ +description: A sequential sampling distribution. + +
+ + +
+ +# dopamine.jax.replay_memory.samplers.SequentialSamplingDistribution + + + + + + + + + +A sequential sampling distribution. + +Inherits From: [`UniformSamplingDistribution`](../../../../dopamine/jax/replay_memory/samplers/UniformSamplingDistribution.md), [`SamplingDistribution`](../../../../dopamine/jax/replay_memory/samplers/SamplingDistribution.md), [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/samplers/UniformSamplingDistribution.md b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/UniformSamplingDistribution.md new file mode 100644 index 00000000..5f642ab3 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/samplers/UniformSamplingDistribution.md @@ -0,0 +1,29 @@ +description: A uniform sampling distribution. + +
+ + +
+ +# dopamine.jax.replay_memory.samplers.UniformSamplingDistribution + + + + + + + + + +A uniform sampling distribution. + +Inherits From: [`SamplingDistribution`](../../../../dopamine/jax/replay_memory/samplers/SamplingDistribution.md), [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/sum_tree.md b/docs/api_docs/python/dopamine/jax/replay_memory/sum_tree.md new file mode 100644 index 00000000..72fdecf3 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/sum_tree.md @@ -0,0 +1,30 @@ +description: Sum Tree. + +
+ + +
+ +# Module: dopamine.jax.replay_memory.sum_tree + + + + + + + + + +Sum Tree. + + + +## Classes + +[`class SumTree`](../../../dopamine/jax/replay_memory/sum_tree/SumTree.md): A vectorized sum tree in numpy. + diff --git a/docs/api_docs/python/dopamine/jax/replay_memory/sum_tree/SumTree.md b/docs/api_docs/python/dopamine/jax/replay_memory/sum_tree/SumTree.md new file mode 100644 index 00000000..3692f2fc --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/replay_memory/sum_tree/SumTree.md @@ -0,0 +1,29 @@ +description: A vectorized sum tree in numpy. + +
+ + +
+ +# dopamine.jax.replay_memory.sum_tree.SumTree + + + + + + + + + +A vectorized sum tree in numpy. + +Inherits From: [`Checkpointable`](../../../../dopamine/jax/checkpointers/Checkpointable.md) + + + + diff --git a/docs/api_docs/python/dopamine/jax/serialization.md b/docs/api_docs/python/dopamine/jax/serialization.md new file mode 100644 index 00000000..b3ad58ad --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/serialization.md @@ -0,0 +1,43 @@ +description: MessagePack serialization hooks. + +
+ + +
+ +# Module: dopamine.jax.serialization + + + + + + + + + +MessagePack serialization hooks. + + +This is necesarry as Orbax doesn't support serializing Numpy structured arrays. +TensorStore (the underlying backend for Orbax) does support structured arrays. +If Orbax ever adds support for structured arrays, we can remove this altogether +and use Orbax to serialize things like the replay buffer without requiring +a custom checkpoint handler (jax/checkpointers.py). + +## Classes + +[`class LongIntegerEncoding`](../../dopamine/jax/serialization/LongIntegerEncoding.md): Encoding for ints longer than 32-bits which MessagePack doesn't support. + +[`class NumpyEncoding`](../../dopamine/jax/serialization/NumpyEncoding.md): Numpy encoding dictionary. + +## Functions + +[`decode(...)`](../../dopamine/jax/serialization/decode.md): Decode encoded object types. + +[`encode(...)`](../../dopamine/jax/serialization/encode.md): Encode object. Encoders will register with `@encode.register`. + diff --git a/docs/api_docs/python/dopamine/jax/serialization/LongIntegerEncoding.md b/docs/api_docs/python/dopamine/jax/serialization/LongIntegerEncoding.md new file mode 100644 index 00000000..434356df --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/serialization/LongIntegerEncoding.md @@ -0,0 +1,27 @@ +description: Encoding for ints longer than 32-bits which MessagePack doesn't support. + +
+ + +
+ +# dopamine.jax.serialization.LongIntegerEncoding + + + + + + + + + +Encoding for ints longer than 32-bits which MessagePack doesn't support. + + + + diff --git a/docs/api_docs/python/dopamine/jax/serialization/NumpyEncoding.md b/docs/api_docs/python/dopamine/jax/serialization/NumpyEncoding.md new file mode 100644 index 00000000..149e07a3 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/serialization/NumpyEncoding.md @@ -0,0 +1,27 @@ +description: Numpy encoding dictionary. + +
+ + +
+ +# dopamine.jax.serialization.NumpyEncoding + + + + + + + + + +Numpy encoding dictionary. + + + + diff --git a/docs/api_docs/python/dopamine/jax/serialization/decode.md b/docs/api_docs/python/dopamine/jax/serialization/decode.md new file mode 100644 index 00000000..957acd97 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/serialization/decode.md @@ -0,0 +1,34 @@ +description: Decode encoded object types. + +
+ + +
+ +# dopamine.jax.serialization.decode + + + + + + + + + +Decode encoded object types. + + +
+dopamine.jax.serialization.decode(
+    obj: Any, chain: Optional[Callable[[Any], Any]] = None
+) -> Any
+
+ + + + diff --git a/docs/api_docs/python/dopamine/jax/serialization/encode.md b/docs/api_docs/python/dopamine/jax/serialization/encode.md new file mode 100644 index 00000000..0205b898 --- /dev/null +++ b/docs/api_docs/python/dopamine/jax/serialization/encode.md @@ -0,0 +1,34 @@ +description: Encode object. Encoders will register with @encode.register. + +
+ + +
+ +# dopamine.jax.serialization.encode + + + + + + + + + +Encode object. Encoders will register with `@encode.register`. + + +
+dopamine.jax.serialization.encode(
+    obj: Any, chain: Optional[Callable[[Any], Any]] = None
+) -> Any
+
+ + + + diff --git a/docs/api_docs/python/dopamine/labs.md b/docs/api_docs/python/dopamine/labs.md new file mode 100644 index 00000000..81345fd3 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs.md @@ -0,0 +1,34 @@ +
+ + +
+ +# Module: dopamine.labs + + + + + + + + + + + + + +## Modules + +[`atari_100k`](../dopamine/labs/atari_100k.md) module + +[`cale`](../dopamine/labs/cale.md) module + +[`moes`](../dopamine/labs/moes.md) module + +[`sac_from_pixels`](../dopamine/labs/sac_from_pixels.md) module + diff --git a/docs/api_docs/python/dopamine/labs/atari_100k.md b/docs/api_docs/python/dopamine/labs/atari_100k.md new file mode 100644 index 00000000..10d8cc38 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/atari_100k.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.labs.atari_100k + + + + + + + + + + + + + +## Modules + +[`atari_100k_rainbow_agent`](../../dopamine/labs/atari_100k/atari_100k_rainbow_agent.md) module: Atari 100k rainbow agent with support for data augmentation. + diff --git a/docs/api_docs/python/dopamine/labs/atari_100k/atari_100k_rainbow_agent.md b/docs/api_docs/python/dopamine/labs/atari_100k/atari_100k_rainbow_agent.md new file mode 100644 index 00000000..c2aecd41 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/atari_100k/atari_100k_rainbow_agent.md @@ -0,0 +1,26 @@ +description: Atari 100k rainbow agent with support for data augmentation. + +
+ + +
+ +# Module: dopamine.labs.atari_100k.atari_100k_rainbow_agent + + + + + + + + + +Atari 100k rainbow agent with support for data augmentation. + + + diff --git a/docs/api_docs/python/dopamine/labs/cale.md b/docs/api_docs/python/dopamine/labs/cale.md new file mode 100644 index 00000000..ab563706 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/cale.md @@ -0,0 +1,32 @@ +
+ + +
+ +# Module: dopamine.labs.cale + + + + + + + + + + + + + +## Modules + +[`ppo_cale`](../../dopamine/labs/cale/ppo_cale.md) module: PPO Agent for CALE. + +[`sac_cale`](../../dopamine/labs/cale/sac_cale.md) module: SAC Agent for CALE. + +[`utils`](../../dopamine/labs/cale/utils.md) module: Common utilities for CALE agents. + diff --git a/docs/api_docs/python/dopamine/labs/cale/ppo_cale.md b/docs/api_docs/python/dopamine/labs/cale/ppo_cale.md new file mode 100644 index 00000000..defb2818 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/cale/ppo_cale.md @@ -0,0 +1,26 @@ +description: PPO Agent for CALE. + +
+ + +
+ +# Module: dopamine.labs.cale.ppo_cale + + + + + + + + + +PPO Agent for CALE. + + + diff --git a/docs/api_docs/python/dopamine/labs/cale/sac_cale.md b/docs/api_docs/python/dopamine/labs/cale/sac_cale.md new file mode 100644 index 00000000..609df2e4 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/cale/sac_cale.md @@ -0,0 +1,26 @@ +description: SAC Agent for CALE. + +
+ + +
+ +# Module: dopamine.labs.cale.sac_cale + + + + + + + + + +SAC Agent for CALE. + + + diff --git a/docs/api_docs/python/dopamine/labs/cale/utils.md b/docs/api_docs/python/dopamine/labs/cale/utils.md new file mode 100644 index 00000000..55aa459d --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/cale/utils.md @@ -0,0 +1,26 @@ +description: Common utilities for CALE agents. + +
+ + +
+ +# Module: dopamine.labs.cale.utils + + + + + + + + + +Common utilities for CALE agents. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes.md b/docs/api_docs/python/dopamine/labs/moes.md new file mode 100644 index 00000000..6b4a73c0 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes.md @@ -0,0 +1,32 @@ +
+ + +
+ +# Module: dopamine.labs.moes + + + + + + + + + + + + + +## Modules + +[`agents`](../../dopamine/labs/moes/agents.md) module + +[`architectures`](../../dopamine/labs/moes/architectures.md) module + +[`google`](../../dopamine/labs/moes/google.md) module + diff --git a/docs/api_docs/python/dopamine/labs/moes/agents.md b/docs/api_docs/python/dopamine/labs/moes/agents.md new file mode 100644 index 00000000..ed3842cd --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/agents.md @@ -0,0 +1,36 @@ +
+ + +
+ +# Module: dopamine.labs.moes.agents + + + + + + + + + + + + + +## Modules + +[`dqn_moe_agent`](../../../dopamine/labs/moes/agents/dqn_moe_agent.md) module: DQN agent that uses MoE networks and logs expert weight histograms. + +[`full_rainbow_moe_agent`](../../../dopamine/labs/moes/agents/full_rainbow_moe_agent.md) module: Compact implementation of the full Rainbow agent in JAX with MoE modules. + +[`losses`](../../../dopamine/labs/moes/agents/losses.md) module: Various losses for training MoEs. + +[`rainbow_100k_moe_agent`](../../../dopamine/labs/moes/agents/rainbow_100k_moe_agent.md) module: Atari 100k rainbow agent with MoE modules. + +[`types`](../../../dopamine/labs/moes/agents/types.md) module: Data types used for MoE losses. + diff --git a/docs/api_docs/python/dopamine/labs/moes/agents/dqn_moe_agent.md b/docs/api_docs/python/dopamine/labs/moes/agents/dqn_moe_agent.md new file mode 100644 index 00000000..70ed8b30 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/agents/dqn_moe_agent.md @@ -0,0 +1,26 @@ +description: DQN agent that uses MoE networks and logs expert weight histograms. + +
+ + +
+ +# Module: dopamine.labs.moes.agents.dqn_moe_agent + + + + + + + + + +DQN agent that uses MoE networks and logs expert weight histograms. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/agents/full_rainbow_moe_agent.md b/docs/api_docs/python/dopamine/labs/moes/agents/full_rainbow_moe_agent.md new file mode 100644 index 00000000..ada80a16 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/agents/full_rainbow_moe_agent.md @@ -0,0 +1,26 @@ +description: Compact implementation of the full Rainbow agent in JAX with MoE modules. + +
+ + +
+ +# Module: dopamine.labs.moes.agents.full_rainbow_moe_agent + + + + + + + + + +Compact implementation of the full Rainbow agent in JAX with MoE modules. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/agents/losses.md b/docs/api_docs/python/dopamine/labs/moes/agents/losses.md new file mode 100644 index 00000000..c29c66de --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/agents/losses.md @@ -0,0 +1,26 @@ +description: Various losses for training MoEs. + +
+ + +
+ +# Module: dopamine.labs.moes.agents.losses + + + + + + + + + +Various losses for training MoEs. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/agents/rainbow_100k_moe_agent.md b/docs/api_docs/python/dopamine/labs/moes/agents/rainbow_100k_moe_agent.md new file mode 100644 index 00000000..9fd14cc5 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/agents/rainbow_100k_moe_agent.md @@ -0,0 +1,26 @@ +description: Atari 100k rainbow agent with MoE modules. + +
+ + +
+ +# Module: dopamine.labs.moes.agents.rainbow_100k_moe_agent + + + + + + + + + +Atari 100k rainbow agent with MoE modules. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/agents/types.md b/docs/api_docs/python/dopamine/labs/moes/agents/types.md new file mode 100644 index 00000000..8fc3bbd9 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/agents/types.md @@ -0,0 +1,26 @@ +description: Data types used for MoE losses. + +
+ + +
+ +# Module: dopamine.labs.moes.agents.types + + + + + + + + + +Data types used for MoE losses. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/architectures.md b/docs/api_docs/python/dopamine/labs/moes/architectures.md new file mode 100644 index 00000000..733d7e8e --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/architectures.md @@ -0,0 +1,36 @@ +
+ + +
+ +# Module: dopamine.labs.moes.architectures + + + + + + + + + + + + + +## Modules + +[`moe`](../../../dopamine/labs/moes/architectures/moe.md) module: Basic MoEs for Dopamine networks(https://arxiv.org/abs/2211.15841). + +[`networks`](../../../dopamine/labs/moes/architectures/networks.md) module: Flax networks that include MoE components. + +[`routers`](../../../dopamine/labs/moes/architectures/routers.md) module: A collection of routers for MoE training. + +[`softmoe`](../../../dopamine/labs/moes/architectures/softmoe.md) module: Implementation of SoftMoE for Dopamine networks. + +[`types`](../../../dopamine/labs/moes/architectures/types.md) module: Return types used by MoE architectures. + diff --git a/docs/api_docs/python/dopamine/labs/moes/architectures/moe.md b/docs/api_docs/python/dopamine/labs/moes/architectures/moe.md new file mode 100644 index 00000000..257d1c52 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/architectures/moe.md @@ -0,0 +1,26 @@ +description: Basic MoEs for Dopamine networks(https://arxiv.org/abs/2211.15841). + +
+ + +
+ +# Module: dopamine.labs.moes.architectures.moe + + + + + + + + + +Basic MoEs for Dopamine networks(https://arxiv.org/abs/2211.15841). + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/architectures/networks.md b/docs/api_docs/python/dopamine/labs/moes/architectures/networks.md new file mode 100644 index 00000000..2c1b986c --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/architectures/networks.md @@ -0,0 +1,26 @@ +description: Flax networks that include MoE components. + +
+ + +
+ +# Module: dopamine.labs.moes.architectures.networks + + + + + + + + + +Flax networks that include MoE components. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/architectures/routers.md b/docs/api_docs/python/dopamine/labs/moes/architectures/routers.md new file mode 100644 index 00000000..8f3fa46a --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/architectures/routers.md @@ -0,0 +1,26 @@ +description: A collection of routers for MoE training. + +
+ + +
+ +# Module: dopamine.labs.moes.architectures.routers + + + + + + + + + +A collection of routers for MoE training. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/architectures/softmoe.md b/docs/api_docs/python/dopamine/labs/moes/architectures/softmoe.md new file mode 100644 index 00000000..fcef4317 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/architectures/softmoe.md @@ -0,0 +1,26 @@ +description: Implementation of SoftMoE for Dopamine networks. + +
+ + +
+ +# Module: dopamine.labs.moes.architectures.softmoe + + + + + + + + + +Implementation of SoftMoE for Dopamine networks. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/architectures/types.md b/docs/api_docs/python/dopamine/labs/moes/architectures/types.md new file mode 100644 index 00000000..5c1b09e6 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/architectures/types.md @@ -0,0 +1,26 @@ +description: Return types used by MoE architectures. + +
+ + +
+ +# Module: dopamine.labs.moes.architectures.types + + + + + + + + + +Return types used by MoE architectures. + + + diff --git a/docs/api_docs/python/dopamine/labs/moes/google.md b/docs/api_docs/python/dopamine/labs/moes/google.md new file mode 100644 index 00000000..0ba017d2 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/moes/google.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.labs.moes.google + + + + + + + + + + + + + +## Modules + +[`dmoe`](../../../dopamine/labs/moes/google/dmoe.md) module: Dropless MoE implementation using Jax's experimental sparsity primitives. + diff --git a/docs/api_docs/python/dopamine/labs/sac_from_pixels.md b/docs/api_docs/python/dopamine/labs/sac_from_pixels.md new file mode 100644 index 00000000..6ea884af --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/sac_from_pixels.md @@ -0,0 +1,30 @@ +
+ + +
+ +# Module: dopamine.labs.sac_from_pixels + + + + + + + + + + + + + +## Modules + +[`continuous_networks`](../../dopamine/labs/sac_from_pixels/continuous_networks.md) module: Networks for continuous control agents. + +[`deepmind_control_lib`](../../dopamine/labs/sac_from_pixels/deepmind_control_lib.md) module: Deepmind Control Suite utilities. + diff --git a/docs/api_docs/python/dopamine/labs/sac_from_pixels/continuous_networks.md b/docs/api_docs/python/dopamine/labs/sac_from_pixels/continuous_networks.md new file mode 100644 index 00000000..a94e1188 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/sac_from_pixels/continuous_networks.md @@ -0,0 +1,26 @@ +description: Networks for continuous control agents. + +
+ + +
+ +# Module: dopamine.labs.sac_from_pixels.continuous_networks + + + + + + + + + +Networks for continuous control agents. + + + diff --git a/docs/api_docs/python/dopamine/labs/sac_from_pixels/deepmind_control_lib.md b/docs/api_docs/python/dopamine/labs/sac_from_pixels/deepmind_control_lib.md new file mode 100644 index 00000000..9b07cc67 --- /dev/null +++ b/docs/api_docs/python/dopamine/labs/sac_from_pixels/deepmind_control_lib.md @@ -0,0 +1,29 @@ +description: Deepmind Control Suite utilities. + +
+ + +
+ +# Module: dopamine.labs.sac_from_pixels.deepmind_control_lib + + + + + + + + + +Deepmind Control Suite utilities. + + +Includes a wrapper class around Deepmind Control Suite environments. +This class makes general Deepmind Control Suite environments conformant with +the API Dopamine is expecting. + diff --git a/docs/api_docs/python/dopamine/metrics.md b/docs/api_docs/python/dopamine/metrics.md new file mode 100644 index 00000000..433ee108 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics.md @@ -0,0 +1,38 @@ +
+ + +
+ +# Module: dopamine.metrics + + + + + + + + + + + + + +## Modules + +[`collector`](../dopamine/metrics/collector.md) module: Base class for metric collectors. + +[`collector_dispatcher`](../dopamine/metrics/collector_dispatcher.md) module: Class that runs a list of Collectors for metrics reporting. + +[`console_collector`](../dopamine/metrics/console_collector.md) module: Collector class for reporting statistics to the console. + +[`pickle_collector`](../dopamine/metrics/pickle_collector.md) module: Collector class for saving iteration statistics to a pickle file. + +[`statistics_instance`](../dopamine/metrics/statistics_instance.md) module: Class containing statistics from training steps. + +[`tensorboard_collector`](../dopamine/metrics/tensorboard_collector.md) module: Collector class for exporting statistics to Tensorboard. + diff --git a/docs/api_docs/python/dopamine/metrics/collector.md b/docs/api_docs/python/dopamine/metrics/collector.md new file mode 100644 index 00000000..5f5d835d --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/collector.md @@ -0,0 +1,41 @@ +description: Base class for metric collectors. + +
+ + +
+ +# Module: dopamine.metrics.collector + + + + + + + + + +Base class for metric collectors. + + +Each Collector should subclass this base class, as the CollectorDispatcher +object expects objects of type Collector. + +The methods to implement are: + - `get_name`: a unique identifier for subdirectory creation. + - `pre_training`: called once before training begins. + - `step`: called once for each training step. The parameter is an object of + type `StatisticsInstance` which contains the statistics of the current + training step. + - `end_training`: called once at the end of training, and passes in a + `StatisticsInstance` containing the statistics of the latest training step. + +## Classes + +[`class Collector`](../../dopamine/metrics/collector/Collector.md): Abstract class for defining metric collectors. + diff --git a/docs/api_docs/python/dopamine/metrics/collector/Collector.md b/docs/api_docs/python/dopamine/metrics/collector/Collector.md new file mode 100644 index 00000000..412b2474 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/collector/Collector.md @@ -0,0 +1,27 @@ +description: Abstract class for defining metric collectors. + +
+ + +
+ +# dopamine.metrics.collector.Collector + + + + + + + + + +Abstract class for defining metric collectors. + + + + diff --git a/docs/api_docs/python/dopamine/metrics/collector_dispatcher.md b/docs/api_docs/python/dopamine/metrics/collector_dispatcher.md new file mode 100644 index 00000000..3286e75b --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/collector_dispatcher.md @@ -0,0 +1,58 @@ +description: Class that runs a list of Collectors for metrics reporting. + +
+ + +
+ +# Module: dopamine.metrics.collector_dispatcher + + + + + + + + + +Class that runs a list of Collectors for metrics reporting. + + +This class is what should be called from the main binary and will call each of +the specified collectors for metrics reporting. + +Each metric collector can be further configured via gin bindings. The +constructor for each desired collector should be passed in as a list when +creating this object. All of the collectors are expected to be subclasses of the +`Collector` base class (defined in `collector.py`). + +#### Example configuration: + + +``` +metrics = CollectorDispatcher(base_dir, num_actions, list_of_constructors) +metrics.pre_training() +for i in range(training_steps): + ... + metrics.step(statistics) +metrics.end_training(statistics) +``` + +The statistics are passed in as a dict that contains +and contains the raw performance statistics for the current iteration. All +processing (such as averaging) will be handled by each of the individual +collectors. + +## Classes + +[`class CollectorDispatcher`](../../dopamine/metrics/collector_dispatcher/CollectorDispatcher.md): Class for collecting and reporting Dopamine metrics. + +## Functions + +[`add_collector(...)`](../../dopamine/metrics/collector_dispatcher/add_collector.md) + diff --git a/docs/api_docs/python/dopamine/metrics/collector_dispatcher/CollectorDispatcher.md b/docs/api_docs/python/dopamine/metrics/collector_dispatcher/CollectorDispatcher.md new file mode 100644 index 00000000..fee68f27 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/collector_dispatcher/CollectorDispatcher.md @@ -0,0 +1,27 @@ +description: Class for collecting and reporting Dopamine metrics. + +
+ + +
+ +# dopamine.metrics.collector_dispatcher.CollectorDispatcher + + + + + + + + + +Class for collecting and reporting Dopamine metrics. + + + + diff --git a/docs/api_docs/python/dopamine/metrics/collector_dispatcher/add_collector.md b/docs/api_docs/python/dopamine/metrics/collector_dispatcher/add_collector.md new file mode 100644 index 00000000..1b7d8e0c --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/collector_dispatcher/add_collector.md @@ -0,0 +1,33 @@ +
+ + +
+ +# dopamine.metrics.collector_dispatcher.add_collector + + + + + + + + + + + + +
+dopamine.metrics.collector_dispatcher.add_collector(
+    name: str,
+    constructor: dopamine.metrics.collector.Collector
+) -> None
+
+ + + + diff --git a/docs/api_docs/python/dopamine/metrics/console_collector.md b/docs/api_docs/python/dopamine/metrics/console_collector.md new file mode 100644 index 00000000..436b6d7a --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/console_collector.md @@ -0,0 +1,30 @@ +description: Collector class for reporting statistics to the console. + +
+ + +
+ +# Module: dopamine.metrics.console_collector + + + + + + + + + +Collector class for reporting statistics to the console. + + + +## Classes + +[`class ConsoleCollector`](../../dopamine/metrics/console_collector/ConsoleCollector.md): Collector class for reporting statistics to the console. + diff --git a/docs/api_docs/python/dopamine/metrics/console_collector/ConsoleCollector.md b/docs/api_docs/python/dopamine/metrics/console_collector/ConsoleCollector.md new file mode 100644 index 00000000..c16503ee --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/console_collector/ConsoleCollector.md @@ -0,0 +1,29 @@ +description: Collector class for reporting statistics to the console. + +
+ + +
+ +# dopamine.metrics.console_collector.ConsoleCollector + + + + + + + + + +Collector class for reporting statistics to the console. + +Inherits From: [`Collector`](../../../dopamine/metrics/collector/Collector.md) + + + + diff --git a/docs/api_docs/python/dopamine/metrics/pickle_collector.md b/docs/api_docs/python/dopamine/metrics/pickle_collector.md new file mode 100644 index 00000000..6815d9b2 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/pickle_collector.md @@ -0,0 +1,30 @@ +description: Collector class for saving iteration statistics to a pickle file. + +
+ + +
+ +# Module: dopamine.metrics.pickle_collector + + + + + + + + + +Collector class for saving iteration statistics to a pickle file. + + + +## Classes + +[`class PickleCollector`](../../dopamine/metrics/pickle_collector/PickleCollector.md): Collector class for reporting statistics to the console. + diff --git a/docs/api_docs/python/dopamine/metrics/pickle_collector/PickleCollector.md b/docs/api_docs/python/dopamine/metrics/pickle_collector/PickleCollector.md new file mode 100644 index 00000000..699fe9f1 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/pickle_collector/PickleCollector.md @@ -0,0 +1,29 @@ +description: Collector class for reporting statistics to the console. + +
+ + +
+ +# dopamine.metrics.pickle_collector.PickleCollector + + + + + + + + + +Collector class for reporting statistics to the console. + +Inherits From: [`Collector`](../../../dopamine/metrics/collector/Collector.md) + + + + diff --git a/docs/api_docs/python/dopamine/metrics/statistics_instance.md b/docs/api_docs/python/dopamine/metrics/statistics_instance.md new file mode 100644 index 00000000..16df90d6 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/statistics_instance.md @@ -0,0 +1,30 @@ +description: Class containing statistics from training steps. + +
+ + +
+ +# Module: dopamine.metrics.statistics_instance + + + + + + + + + +Class containing statistics from training steps. + + + +## Classes + +[`class StatisticsInstance`](../../dopamine/metrics/statistics_instance/StatisticsInstance.md): Statistics to be passed to each of the collectors. + diff --git a/docs/api_docs/python/dopamine/metrics/statistics_instance/StatisticsInstance.md b/docs/api_docs/python/dopamine/metrics/statistics_instance/StatisticsInstance.md new file mode 100644 index 00000000..36d54e53 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/statistics_instance/StatisticsInstance.md @@ -0,0 +1,67 @@ +description: Statistics to be passed to each of the collectors. + +
+ + +
+ +# dopamine.metrics.statistics_instance.StatisticsInstance + + + + + + + + + +Statistics to be passed to each of the collectors. + + + + + + + + + + + + + + + + + + + + + + + + +

Attributes

+`name` + +Dataclass field +
+`value` + +Dataclass field +
+`step` + +Dataclass field +
+`type` + +Dataclass field +
+ + + diff --git a/docs/api_docs/python/dopamine/metrics/tensorboard_collector.md b/docs/api_docs/python/dopamine/metrics/tensorboard_collector.md new file mode 100644 index 00000000..28593d38 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/tensorboard_collector.md @@ -0,0 +1,30 @@ +description: Collector class for exporting statistics to Tensorboard. + +
+ + +
+ +# Module: dopamine.metrics.tensorboard_collector + + + + + + + + + +Collector class for exporting statistics to Tensorboard. + + + +## Classes + +[`class TensorboardCollector`](../../dopamine/metrics/tensorboard_collector/TensorboardCollector.md): Collector class for reporting statistics on Tensorboard. + diff --git a/docs/api_docs/python/dopamine/metrics/tensorboard_collector/TensorboardCollector.md b/docs/api_docs/python/dopamine/metrics/tensorboard_collector/TensorboardCollector.md new file mode 100644 index 00000000..1426c106 --- /dev/null +++ b/docs/api_docs/python/dopamine/metrics/tensorboard_collector/TensorboardCollector.md @@ -0,0 +1,29 @@ +description: Collector class for reporting statistics on Tensorboard. + +
+ + +
+ +# dopamine.metrics.tensorboard_collector.TensorboardCollector + + + + + + + + + +Collector class for reporting statistics on Tensorboard. + +Inherits From: [`Collector`](../../../dopamine/metrics/collector/Collector.md) + + + + diff --git a/docs/api_docs/python/dopamine/tf.md b/docs/api_docs/python/dopamine/tf.md new file mode 100644 index 00000000..25658c80 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf.md @@ -0,0 +1,30 @@ +
+ + +
+ +# Module: dopamine.tf + + + + + + + + + + + + + +## Modules + +[`agents`](../dopamine/tf/agents.md) module + +[`replay_memory`](../dopamine/tf/replay_memory.md) module + diff --git a/docs/api_docs/python/dopamine/tf/agents.md b/docs/api_docs/python/dopamine/tf/agents.md new file mode 100644 index 00000000..17c6d746 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents.md @@ -0,0 +1,32 @@ +
+ + +
+ +# Module: dopamine.tf.agents + + + + + + + + + + + + + +## Modules + +[`dqn`](../../dopamine/tf/agents/dqn.md) module + +[`implicit_quantile`](../../dopamine/tf/agents/implicit_quantile.md) module + +[`rainbow`](../../dopamine/tf/agents/rainbow.md) module + diff --git a/docs/api_docs/python/dopamine/tf/agents/dqn.md b/docs/api_docs/python/dopamine/tf/agents/dqn.md new file mode 100644 index 00000000..079cee7d --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/dqn.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.tf.agents.dqn + + + + + + + + + + + + + +## Modules + +[`dqn_agent`](../../../dopamine/tf/agents/dqn/dqn_agent.md) module: Compact implementation of a DQN agent. + diff --git a/docs/api_docs/python/dopamine/tf/agents/dqn/dqn_agent.md b/docs/api_docs/python/dopamine/tf/agents/dqn/dqn_agent.md new file mode 100644 index 00000000..26de4e1c --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/dqn/dqn_agent.md @@ -0,0 +1,30 @@ +description: Compact implementation of a DQN agent. + +
+ + +
+ +# Module: dopamine.tf.agents.dqn.dqn_agent + + + + + + + + + +Compact implementation of a DQN agent. + + + +## Classes + +[`class DQNAgent`](../../../../dopamine/tf/agents/dqn/dqn_agent/DQNAgent.md): An implementation of the DQN agent. + diff --git a/docs/api_docs/python/dopamine/tf/agents/dqn/dqn_agent/DQNAgent.md b/docs/api_docs/python/dopamine/tf/agents/dqn/dqn_agent/DQNAgent.md new file mode 100644 index 00000000..9b52a38f --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/dqn/dqn_agent/DQNAgent.md @@ -0,0 +1,27 @@ +description: An implementation of the DQN agent. + +
+ + +
+ +# dopamine.tf.agents.dqn.dqn_agent.DQNAgent + + + + + + + + + +An implementation of the DQN agent. + + + + diff --git a/docs/api_docs/python/dopamine/tf/agents/implicit_quantile.md b/docs/api_docs/python/dopamine/tf/agents/implicit_quantile.md new file mode 100644 index 00000000..d159753e --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/implicit_quantile.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.tf.agents.implicit_quantile + + + + + + + + + + + + + +## Modules + +[`implicit_quantile_agent`](../../../dopamine/tf/agents/implicit_quantile/implicit_quantile_agent.md) module: The implicit quantile networks (IQN) agent. + diff --git a/docs/api_docs/python/dopamine/tf/agents/implicit_quantile/implicit_quantile_agent.md b/docs/api_docs/python/dopamine/tf/agents/implicit_quantile/implicit_quantile_agent.md new file mode 100644 index 00000000..97b7e6a4 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/implicit_quantile/implicit_quantile_agent.md @@ -0,0 +1,32 @@ +description: The implicit quantile networks (IQN) agent. + +
+ + +
+ +# Module: dopamine.tf.agents.implicit_quantile.implicit_quantile_agent + + + + + + + + + +The implicit quantile networks (IQN) agent. + + +The agent follows the description given in "Implicit Quantile Networks for +Distributional RL" (Dabney et. al, 2018). + +## Classes + +[`class ImplicitQuantileAgent`](../../../../dopamine/tf/agents/implicit_quantile/implicit_quantile_agent/ImplicitQuantileAgent.md): An extension of Rainbow to perform implicit quantile regression. + diff --git a/docs/api_docs/python/dopamine/tf/agents/implicit_quantile/implicit_quantile_agent/ImplicitQuantileAgent.md b/docs/api_docs/python/dopamine/tf/agents/implicit_quantile/implicit_quantile_agent/ImplicitQuantileAgent.md new file mode 100644 index 00000000..86838bca --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/implicit_quantile/implicit_quantile_agent/ImplicitQuantileAgent.md @@ -0,0 +1,29 @@ +description: An extension of Rainbow to perform implicit quantile regression. + +
+ + +
+ +# dopamine.tf.agents.implicit_quantile.implicit_quantile_agent.ImplicitQuantileAgent + + + + + + + + + +An extension of Rainbow to perform implicit quantile regression. + +Inherits From: [`RainbowAgent`](../../../../../dopamine/tf/agents/rainbow/rainbow_agent/RainbowAgent.md), [`DQNAgent`](../../../../../dopamine/tf/agents/dqn/dqn_agent/DQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/tf/agents/rainbow.md b/docs/api_docs/python/dopamine/tf/agents/rainbow.md new file mode 100644 index 00000000..42d7a7b8 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/rainbow.md @@ -0,0 +1,28 @@ +
+ + +
+ +# Module: dopamine.tf.agents.rainbow + + + + + + + + + + + + + +## Modules + +[`rainbow_agent`](../../../dopamine/tf/agents/rainbow/rainbow_agent.md) module: Compact implementation of a simplified Rainbow agent. + diff --git a/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent.md b/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent.md new file mode 100644 index 00000000..47336758 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent.md @@ -0,0 +1,51 @@ +description: Compact implementation of a simplified Rainbow agent. + +
+ + +
+ +# Module: dopamine.tf.agents.rainbow.rainbow_agent + + + + + + + + + +Compact implementation of a simplified Rainbow agent. + + +Specifically, we implement the following components from Rainbow: + + * n-step updates; + * prioritized replay; and + * distributional RL. + +These three components were found to significantly impact the performance of +the Atari game-playing agent. + +Furthermore, our implementation does away with some minor hyperparameter +choices. Specifically, we + + * keep the beta exponent fixed at beta=0.5, rather than increase it linearly; + * remove the alpha parameter, which was set to alpha=0.5 throughout the paper. + +Details in "Rainbow: Combining Improvements in Deep Reinforcement Learning" by +Hessel et al. (2018). + +## Classes + +[`class RainbowAgent`](../../../../dopamine/tf/agents/rainbow/rainbow_agent/RainbowAgent.md): A compact implementation of a simplified Rainbow agent. + +## Functions + +[`project_distribution(...)`](../../../../dopamine/tf/agents/rainbow/rainbow_agent/project_distribution.md): Projects a batch of (support, weights) onto target_support. + diff --git a/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent/RainbowAgent.md b/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent/RainbowAgent.md new file mode 100644 index 00000000..e1c7ccf1 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent/RainbowAgent.md @@ -0,0 +1,29 @@ +description: A compact implementation of a simplified Rainbow agent. + +
+ + +
+ +# dopamine.tf.agents.rainbow.rainbow_agent.RainbowAgent + + + + + + + + + +A compact implementation of a simplified Rainbow agent. + +Inherits From: [`DQNAgent`](../../../../../dopamine/tf/agents/dqn/dqn_agent/DQNAgent.md) + + + + diff --git a/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent/project_distribution.md b/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent/project_distribution.md new file mode 100644 index 00000000..9a706bdf --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/agents/rainbow/rainbow_agent/project_distribution.md @@ -0,0 +1,127 @@ +description: Projects a batch of (support, weights) onto target_support. + +
+ + +
+ +# dopamine.tf.agents.rainbow.rainbow_agent.project_distribution + + + + + + + + + +Projects a batch of (support, weights) onto target_support. + + +
+dopamine.tf.agents.rainbow.rainbow_agent.project_distribution(
+    supports, weights, target_support, validate_args=False
+)
+
+ + + + + +Based on equation (7) in (Bellemare et al., 2017): + https://arxiv.org/abs/1707.06887 +In the rest of the comments we will refer to this equation simply as Eq7. + +This code is not easy to digest, so we will use a running example to clarify +what is going on, with the following sample inputs: + + * supports = [[0, 2, 4, 6, 8], + [1, 3, 4, 5, 6]] + * weights = [[0.1, 0.6, 0.1, 0.1, 0.1], + [0.1, 0.2, 0.5, 0.1, 0.1]] + * target_support = [4, 5, 6, 7, 8] + +In the code below, comments preceded with 'Ex:' will be referencing the above +values. + + + + + + + + + + + + + + + + + + + +

Args

+`supports` + +Tensor of shape (batch_size, num_dims) defining supports for the +distribution. +
+`weights` + +Tensor of shape (batch_size, num_dims) defining weights on the +original support points. Although for the CategoricalDQN agent these +weights are probabilities, it is not required that they are. +
+`target_support` + +Tensor of shape (num_dims) defining support of the projected +distribution. The values must be monotonically increasing. Vmin and Vmax +will be inferred from the first and last elements of this tensor, +respectively. The values in this tensor must be equally spaced. +
+`validate_args` + +Whether we will verify the contents of the target_support +parameter. +
+ + + + + + + + + + + +

Returns

+A Tensor of shape (batch_size, num_dims) with the projection of a batch of +(support, weights) onto target_support. +
+ + + + + + + + + + + + +

Raises

+`ValueError` + +If target_support has no dimensions, or if shapes of supports, +weights, and target_support are incompatible. +
+ diff --git a/docs/api_docs/python/dopamine/tf/replay_memory.md b/docs/api_docs/python/dopamine/tf/replay_memory.md new file mode 100644 index 00000000..ae08556b --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory.md @@ -0,0 +1,32 @@ +
+ + +
+ +# Module: dopamine.tf.replay_memory + + + + + + + + + + + + + +## Modules + +[`circular_replay_buffer`](../../dopamine/tf/replay_memory/circular_replay_buffer.md) module: The standard DQN replay memory. + +[`prioritized_replay_buffer`](../../dopamine/tf/replay_memory/prioritized_replay_buffer.md) module: An implementation of Prioritized Experience Replay (PER). + +[`sum_tree`](../../dopamine/tf/replay_memory/sum_tree.md) module: A sum tree data structure. + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer.md b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer.md new file mode 100644 index 00000000..d8bb831d --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer.md @@ -0,0 +1,39 @@ +description: The standard DQN replay memory. + +
+ + +
+ +# Module: dopamine.tf.replay_memory.circular_replay_buffer + + + + + + + + + +The standard DQN replay memory. + + +This implementation is an out-of-graph replay memory + in-graph wrapper. It +supports vanilla n-step updates of the form typically found in the literature, +i.e. where rewards are accumulated for n steps and the intermediate trajectory +is not exposed to the agent. This does not allow, for example, performing +off-policy corrections. + +## Classes + +[`class OutOfGraphReplayBuffer`](../../../dopamine/tf/replay_memory/circular_replay_buffer/OutOfGraphReplayBuffer.md): A simple out-of-graph Replay Buffer. + +[`class ReplayElement`](../../../dopamine/tf/replay_memory/circular_replay_buffer/ReplayElement.md): shape_type(name, shape, type) + +[`class WrappedReplayBuffer`](../../../dopamine/tf/replay_memory/circular_replay_buffer/WrappedReplayBuffer.md): Wrapper of OutOfGraphReplayBuffer with an in graph sampling mechanism. + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/OutOfGraphReplayBuffer.md b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/OutOfGraphReplayBuffer.md new file mode 100644 index 00000000..185b85f0 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/OutOfGraphReplayBuffer.md @@ -0,0 +1,70 @@ +description: A simple out-of-graph Replay Buffer. + +
+ + +
+ +# dopamine.tf.replay_memory.circular_replay_buffer.OutOfGraphReplayBuffer + + + + + + + + + +A simple out-of-graph Replay Buffer. + + + +Stores transitions, state, action, reward, next_state, terminal (and any +extra contents specified) in a circular buffer and provides a uniform +transition sampling function. + +When the states consist of stacks of observations storing the states is +inefficient. This class writes observations and constructs the stacked states +at sample time. + + + + + + + + + + + + + + + + + + +

Attributes

+`add_count` + +int, counter of how many transitions have been added (including +the blank ones at the beginning of an episode). +
+`invalid_range` + +np.array, an array with the indices of cursor-related invalid +transitions +
+`episode_end_indices` + +set[int], a set of indices corresponding to the end of +an episode. +
+ + + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/ReplayElement.md b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/ReplayElement.md new file mode 100644 index 00000000..1cf3a14f --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/ReplayElement.md @@ -0,0 +1,27 @@ +description: shape_type(name, shape, type) + +
+ + +
+ +# dopamine.tf.replay_memory.circular_replay_buffer.ReplayElement + + + + + + + + + +shape_type(name, shape, type) + + + + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/WrappedReplayBuffer.md b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/WrappedReplayBuffer.md new file mode 100644 index 00000000..105ccabf --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/circular_replay_buffer/WrappedReplayBuffer.md @@ -0,0 +1,46 @@ +description: Wrapper of OutOfGraphReplayBuffer with an in graph sampling mechanism. + +
+ + +
+ +# dopamine.tf.replay_memory.circular_replay_buffer.WrappedReplayBuffer + + + + + + + + + +Wrapper of OutOfGraphReplayBuffer with an in graph sampling mechanism. + + + + + + + + + + + + +

Usage

+To add a transition: call the add function. + +To sample a batch: Construct operations that depend on any of the + tensors is the transition dictionary. Every sess.run + that requires any of these tensors will sample a new + transition. +
+ + + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer.md b/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer.md new file mode 100644 index 00000000..e2944597 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer.md @@ -0,0 +1,35 @@ +description: An implementation of Prioritized Experience Replay (PER). + +
+ + +
+ +# Module: dopamine.tf.replay_memory.prioritized_replay_buffer + + + + + + + + + +An implementation of Prioritized Experience Replay (PER). + + +This implementation is based on the paper "Prioritized Experience Replay" +by Tom Schaul et al. (2015). Many thanks to Tom Schaul, John Quan, and Matteo +Hessel for providing useful pointers on the algorithm and its implementation. + +## Classes + +[`class OutOfGraphPrioritizedReplayBuffer`](../../../dopamine/tf/replay_memory/prioritized_replay_buffer/OutOfGraphPrioritizedReplayBuffer.md): An out-of-graph Replay Buffer for Prioritized Experience Replay. + +[`class WrappedPrioritizedReplayBuffer`](../../../dopamine/tf/replay_memory/prioritized_replay_buffer/WrappedPrioritizedReplayBuffer.md): Wrapper of OutOfGraphPrioritizedReplayBuffer with in-graph sampling. + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer/OutOfGraphPrioritizedReplayBuffer.md b/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer/OutOfGraphPrioritizedReplayBuffer.md new file mode 100644 index 00000000..26ae8e68 --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer/OutOfGraphPrioritizedReplayBuffer.md @@ -0,0 +1,30 @@ +description: An out-of-graph Replay Buffer for Prioritized Experience Replay. + +
+ + +
+ +# dopamine.tf.replay_memory.prioritized_replay_buffer.OutOfGraphPrioritizedReplayBuffer + + + + + + + + + +An out-of-graph Replay Buffer for Prioritized Experience Replay. + +Inherits From: [`OutOfGraphReplayBuffer`](../../../../dopamine/tf/replay_memory/circular_replay_buffer/OutOfGraphReplayBuffer.md) + + + +See circular_replay_buffer.py for details. + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer/WrappedPrioritizedReplayBuffer.md b/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer/WrappedPrioritizedReplayBuffer.md new file mode 100644 index 00000000..1e0dcd6e --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/prioritized_replay_buffer/WrappedPrioritizedReplayBuffer.md @@ -0,0 +1,47 @@ +description: Wrapper of OutOfGraphPrioritizedReplayBuffer with in-graph sampling. + +
+ + +
+ +# dopamine.tf.replay_memory.prioritized_replay_buffer.WrappedPrioritizedReplayBuffer + + + + + + + + + +Wrapper of OutOfGraphPrioritizedReplayBuffer with in-graph sampling. + +Inherits From: [`WrappedReplayBuffer`](../../../../dopamine/tf/replay_memory/circular_replay_buffer/WrappedReplayBuffer.md) + + + + + + + + + + + + +

Usage

+* To add a transition: Call the add function. + +* To sample a batch: Query any of the tensors in the transition dictionary. + Every sess.run that requires any of these tensors will + sample a new transition. +
+ + + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/sum_tree.md b/docs/api_docs/python/dopamine/tf/replay_memory/sum_tree.md new file mode 100644 index 00000000..08265c9b --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/sum_tree.md @@ -0,0 +1,32 @@ +description: A sum tree data structure. + +
+ + +
+ +# Module: dopamine.tf.replay_memory.sum_tree + + + + + + + + + +A sum tree data structure. + + +Used for prioritized experience replay. See prioritized_replay_buffer.py +and Schaul et al. (2015). + +## Classes + +[`class SumTree`](../../../dopamine/tf/replay_memory/sum_tree/SumTree.md): A sum tree data structure for storing replay priorities. + diff --git a/docs/api_docs/python/dopamine/tf/replay_memory/sum_tree/SumTree.md b/docs/api_docs/python/dopamine/tf/replay_memory/sum_tree/SumTree.md new file mode 100644 index 00000000..10382e5d --- /dev/null +++ b/docs/api_docs/python/dopamine/tf/replay_memory/sum_tree/SumTree.md @@ -0,0 +1,57 @@ +description: A sum tree data structure for storing replay priorities. + +
+ + +
+ +# dopamine.tf.replay_memory.sum_tree.SumTree + + + + + + + + + +A sum tree data structure for storing replay priorities. + + + +A sum tree is a complete binary tree whose leaves contain values called +priorities. Internal nodes maintain the sum of the priorities of all leaf +nodes in their subtree. + +For capacity = 4, the tree may look like this: + + +---+ + |2.5| + +-+-+ + | + +-------+--------+ + | | + +-+-+ +-+-+ + |1.5| |1.0| + +-+-+ +-+-+ + | | + +----+----+ +----+----+ + | | | | ++-+-+ +-+-+ +-+-+ +-+-+ +|0.5| |1.0| |0.5| |0.5| ++---+ +---+ +---+ +---+ + +This is stored in a list of numpy arrays: +self.nodes = [ [2.5], [1.5, 1], [0.5, 1, 0.5, 0.5] ] + +For conciseness, we allocate arrays as powers of two, and pad the excess +elements with zero values. + +This is similar to the usual array-based representation of a complete binary +tree, but is a little more user-friendly. + diff --git a/docs/api_docs/python/dopamine/utils.md b/docs/api_docs/python/dopamine/utils.md new file mode 100644 index 00000000..a22a9c95 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils.md @@ -0,0 +1,40 @@ +
+ + +
+ +# Module: dopamine.utils + + + + + + + + + + + + + +## Modules + +[`agent_visualizer`](../dopamine/utils/agent_visualizer.md) module: Code to visualize different aspects of an agent's behaviour. + +[`atari_plotter`](../dopamine/utils/atari_plotter.md) module: AtariPlotter used for rendering Atari 2600 frames. + +[`bar_plotter`](../dopamine/utils/bar_plotter.md) module: BarPlotter used for drawing bar plots. + +[`example_viz`](../dopamine/utils/example_viz.md) module: Sample file to generate visualizations. + +[`example_viz_lib`](../dopamine/utils/example_viz_lib.md) module: Library used by example_viz.py to generate visualizations. + +[`line_plotter`](../dopamine/utils/line_plotter.md) module: LinePlotter used for drawing line plots. + +[`plotter`](../dopamine/utils/plotter.md) module: Base class for plotters. + diff --git a/docs/api_docs/python/dopamine/utils/agent_visualizer.md b/docs/api_docs/python/dopamine/utils/agent_visualizer.md new file mode 100644 index 00000000..4f4ad807 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/agent_visualizer.md @@ -0,0 +1,34 @@ +description: Code to visualize different aspects of an agent's behaviour. + +
+ + +
+ +# Module: dopamine.utils.agent_visualizer + + + + + + + + + +Code to visualize different aspects of an agent's behaviour. + + +This file defines the class AgentVisualizer, which allows one to combine +a number of Plotter objects into a series of single images, generated during +agent interaction with the environment. +If requested, this class will combine the image files into a movie. + +## Classes + +[`class AgentVisualizer`](../../dopamine/utils/agent_visualizer/AgentVisualizer.md): Code to visualize an agent's behaviour. + diff --git a/docs/api_docs/python/dopamine/utils/agent_visualizer/AgentVisualizer.md b/docs/api_docs/python/dopamine/utils/agent_visualizer/AgentVisualizer.md new file mode 100644 index 00000000..6fd60817 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/agent_visualizer/AgentVisualizer.md @@ -0,0 +1,27 @@ +description: Code to visualize an agent's behaviour. + +
+ + +
+ +# dopamine.utils.agent_visualizer.AgentVisualizer + + + + + + + + + +Code to visualize an agent's behaviour. + + + + diff --git a/docs/api_docs/python/dopamine/utils/atari_plotter.md b/docs/api_docs/python/dopamine/utils/atari_plotter.md new file mode 100644 index 00000000..e2088b9e --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/atari_plotter.md @@ -0,0 +1,30 @@ +description: AtariPlotter used for rendering Atari 2600 frames. + +
+ + +
+ +# Module: dopamine.utils.atari_plotter + + + + + + + + + +AtariPlotter used for rendering Atari 2600 frames. + + + +## Classes + +[`class AtariPlotter`](../../dopamine/utils/atari_plotter/AtariPlotter.md): A Plotter for rendering Atari 2600 frames. + diff --git a/docs/api_docs/python/dopamine/utils/atari_plotter/AtariPlotter.md b/docs/api_docs/python/dopamine/utils/atari_plotter/AtariPlotter.md new file mode 100644 index 00000000..b2df5eb3 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/atari_plotter/AtariPlotter.md @@ -0,0 +1,29 @@ +description: A Plotter for rendering Atari 2600 frames. + +
+ + +
+ +# dopamine.utils.atari_plotter.AtariPlotter + + + + + + + + + +A Plotter for rendering Atari 2600 frames. + +Inherits From: [`Plotter`](../../../dopamine/utils/plotter/Plotter.md) + + + + diff --git a/docs/api_docs/python/dopamine/utils/bar_plotter.md b/docs/api_docs/python/dopamine/utils/bar_plotter.md new file mode 100644 index 00000000..910cd698 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/bar_plotter.md @@ -0,0 +1,33 @@ +description: BarPlotter used for drawing bar plots. + +
+ + +
+ +# Module: dopamine.utils.bar_plotter + + + + + + + + + +BarPlotter used for drawing bar plots. + + +Note that a side effect of using this class is to change the font used by +matplotlib. Unless you're planning to use matplotlib elsewhere in your code, +this should be a non-issue. + +## Classes + +[`class BarPlotter`](../../dopamine/utils/bar_plotter/BarPlotter.md): A Plotter for generating bar plots. + diff --git a/docs/api_docs/python/dopamine/utils/bar_plotter/BarPlotter.md b/docs/api_docs/python/dopamine/utils/bar_plotter/BarPlotter.md new file mode 100644 index 00000000..a53d6b51 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/bar_plotter/BarPlotter.md @@ -0,0 +1,29 @@ +description: A Plotter for generating bar plots. + +
+ + +
+ +# dopamine.utils.bar_plotter.BarPlotter + + + + + + + + + +A Plotter for generating bar plots. + +Inherits From: [`Plotter`](../../../dopamine/utils/plotter/Plotter.md) + + + + diff --git a/docs/api_docs/python/dopamine/utils/example_viz.md b/docs/api_docs/python/dopamine/utils/example_viz.md new file mode 100644 index 00000000..7f5338e6 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/example_viz.md @@ -0,0 +1,49 @@ +description: Sample file to generate visualizations. + +
+ + +
+ +# Module: dopamine.utils.example_viz + + + + + + + + + +Sample file to generate visualizations. + + +To run, point FLAGS.restore_checkpoint to the TensorFlow checkpoint of a +trained agent. As an example, you can download to `/tmp/checkpoints` the files +linked below: + + * https://storage.cloud.google.com/download-dopamine-rl/colab/samples/rainbow/SpaceInvaders_v4/checkpoints/tf_ckpt-199.data-00000-of-00001 + * https://storage.cloud.google.com/download-dopamine-rl/colab/samples/rainbow/SpaceInvaders_v4/checkpoints/tf_ckpt-199.index + * https://storage.cloud.google.com/download-dopamine-rl/colab/samples/rainbow/SpaceInvaders_v4/checkpoints/tf_ckpt-199.meta + + +You can then run the binary with: + +``` +python example_viz.py \ + --agent='rainbow' \ + --game='SpaceInvaders' \ + --num_steps=1000 \ + --root_dir='/tmp/dopamine' \ + --restore_checkpoint=/tmp/checkpoints/colab_samples_rainbow_SpaceInvaders_v4_checkpoints_tf_ckpt-199 +``` + +## Modules + +[`example_viz_lib`](../../dopamine/utils/example_viz_lib.md) module: Library used by example_viz.py to generate visualizations. + diff --git a/docs/api_docs/python/dopamine/utils/example_viz_lib.md b/docs/api_docs/python/dopamine/utils/example_viz_lib.md new file mode 100644 index 00000000..e17be36c --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/example_viz_lib.md @@ -0,0 +1,47 @@ +description: Library used by example_viz.py to generate visualizations. + +
+ + +
+ +# Module: dopamine.utils.example_viz_lib + + + + + + + + + +Library used by example_viz.py to generate visualizations. + + + + +This file illustrates the following: + - How to subclass an existing agent to add visualization functionality. + - For DQN we visualize the cumulative rewards and the Q-values for each + action (MyDQNAgent). + - For Rainbow we visualize the cumulative rewards and the Q-value + distributions for each action (MyRainbowAgent). + - How to subclass Runner to run in eval mode, lay out the different subplots, + generate the visualizations, and compile them into a video (MyRunner). + - The function `run()` is the main entrypoint for running everything. + +## Functions + +[`create_runner(...)`](../../dopamine/utils/example_viz_lib/create_runner.md) + diff --git a/docs/api_docs/python/dopamine/utils/example_viz_lib/create_runner.md b/docs/api_docs/python/dopamine/utils/example_viz_lib/create_runner.md new file mode 100644 index 00000000..6322ba6a --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/example_viz_lib/create_runner.md @@ -0,0 +1,43 @@ +
+ + +
+ +# dopamine.utils.example_viz_lib.create_runner + + + + + + + + + + + + + + +
+dopamine.utils.example_viz_lib.create_runner(
+    base_dir,
+    trained_agent_ckpt_path,
+    agent='dqn',
+    use_legacy_checkpoint=False
+)
+
+ + + + diff --git a/docs/api_docs/python/dopamine/utils/line_plotter.md b/docs/api_docs/python/dopamine/utils/line_plotter.md new file mode 100644 index 00000000..851eeb18 --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/line_plotter.md @@ -0,0 +1,33 @@ +description: LinePlotter used for drawing line plots. + +
+ + +
+ +# Module: dopamine.utils.line_plotter + + + + + + + + + +LinePlotter used for drawing line plots. + + +Note that a side effect of using this class is to change the font used by +matplotlib. Unless you're planning to use matplotlib elsewhere in your code, +this should be a non-issue. + +## Classes + +[`class LinePlotter`](../../dopamine/utils/line_plotter/LinePlotter.md): A Plotter for generating line plots. + diff --git a/docs/api_docs/python/dopamine/utils/line_plotter/LinePlotter.md b/docs/api_docs/python/dopamine/utils/line_plotter/LinePlotter.md new file mode 100644 index 00000000..151a8e7b --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/line_plotter/LinePlotter.md @@ -0,0 +1,29 @@ +description: A Plotter for generating line plots. + +
+ + +
+ +# dopamine.utils.line_plotter.LinePlotter + + + + + + + + + +A Plotter for generating line plots. + +Inherits From: [`Plotter`](../../../dopamine/utils/plotter/Plotter.md) + + + + diff --git a/docs/api_docs/python/dopamine/utils/plotter.md b/docs/api_docs/python/dopamine/utils/plotter.md new file mode 100644 index 00000000..7a9994fc --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/plotter.md @@ -0,0 +1,37 @@ +description: Base class for plotters. + +
+ + +
+ +# Module: dopamine.utils.plotter + + + + + + + + + +Base class for plotters. + + +This class provides the core functionality for Plotter objects. Specifically, it +initializes `self.parameters` with the values passed through the constructor or +with the provided defaults (specified in each child class), and specifies the +abstract `draw()` method, which child classes will need to implement. + +This class also provides a helper function `_setup_plot` for Plotters based on +matplotlib. + +## Classes + +[`class Plotter`](../../dopamine/utils/plotter/Plotter.md): Abstract base class for plotters. + diff --git a/docs/api_docs/python/dopamine/utils/plotter/Plotter.md b/docs/api_docs/python/dopamine/utils/plotter/Plotter.md new file mode 100644 index 00000000..bffe997b --- /dev/null +++ b/docs/api_docs/python/dopamine/utils/plotter/Plotter.md @@ -0,0 +1,27 @@ +description: Abstract base class for plotters. + +
+ + +
+ +# dopamine.utils.plotter.Plotter + + + + + + + + + +Abstract base class for plotters. + + + + diff --git a/docs/changelist.md b/docs/changelist.md index 623284a0..746b51fc 100644 --- a/docs/changelist.md +++ b/docs/changelist.md @@ -6,6 +6,33 @@ Note: This changelist attempts to summarize major changes to the Dopamine library. For a more fine-grained changelist, consider looking through the [commit history](https://github.com/google/dopamine/commits/master). +* **25/10/2024:** A few major changes: + * Making Jax agents the default. + * Add support for Impala network architectures. + * Deprecating TensorFlow agents (now stored in + [tf](https://github.com/google/dopamine/tree/master/dopamine/tf) + directory). + * Adding new replay buffer (twice as fast!). + * Added an implementation of PPO. + * Switching from (deprecated) + [`atari_py`](https://github.com/openai/atari-py) to + [`ale_py`](https://github.com/Farama-Foundation/Arcade-Learning-Environment). + * Switching from (deprecated) + [OpenAI-Gym](https://github.com/openai/gym) to + [Gymnasium](https://github.com/Farama-Foundation/Gymnasium). + * Update documentation page (much needed). + * Update baselines plots to use all Jax agents, as well as those trained with + the Impala architecture. + * Add code for many of our published papers 😊. + +* **22/09/2022:** Finish adding new setup for statistics collection (inside + [metrics](https://github.com/google/dopamine/tree/master/dopamine/metrics) + directory). + +* **20/05/2022:** Make MSE the default loss type for Jax DQN. + +* **18/10/2021:** Added support for DM-Control. + * **21/09/2021:** Added Dockerfiles and instructions for using Dopamine with docker. diff --git a/dopamine/colab/README.md b/dopamine/colab/README.md index 5fb1b39d..b1a37875 100644 --- a/dopamine/colab/README.md +++ b/dopamine/colab/README.md @@ -11,7 +11,7 @@ We also provide a set of colabs to help illustrate how you can use Dopamine. In this [colab](https://colab.research.google.com/github/google/dopamine/blob/master/dopamine/colab/agents.ipynb) we illustrate how to create a new agent by either subclassing -[`DQN`](https://github.com/google/dopamine/blob/master/dopamine/agents/dqn/dqn_agent.py) +[`DQN`](https://github.com/google/dopamine/blob/master/dopamine/jax/agents/dqn/dqn_agent.py) or by creating a new agent from scratch. ## Loading statistics diff --git a/dopamine/colab/agents.ipynb b/dopamine/colab/agents.ipynb index f05da1e6..13ca6463 100644 --- a/dopamine/colab/agents.ipynb +++ b/dopamine/colab/agents.ipynb @@ -67,7 +67,7 @@ "\n", "import numpy as np\n", "import os\n", - "from dopamine.agents.dqn import dqn_agent\n", + "from dopamine.jax.agents.dqn import dqn_agent\n", "from dopamine.discrete_domains import run_experiment\n", "from dopamine.colab import utils as colab_utils\n", "from absl import flags\n", @@ -118,9 +118,9 @@ "LOG_PATH = os.path.join(BASE_PATH, 'random_dqn', GAME)\n", "\n", "class MyRandomDQNAgent(dqn_agent.DQNAgent):\n", - " def __init__(self, sess, num_actions):\n", + " def __init__(self, num_actions):\n", " \"\"\"This maintains all the DQN default argument values.\"\"\"\n", - " super().__init__(sess, num_actions)\n", + " super().__init__(num_actions)\n", "\n", " def step(self, reward, observation):\n", " \"\"\"Calls the step function of the parent class, but returns a random action.\n", @@ -128,9 +128,9 @@ " super().step(reward, observation)\n", " return np.random.randint(self.num_actions)\n", "\n", - "def create_random_dqn_agent(sess, environment, summary_writer=None):\n", + "def create_random_dqn_agent(unused_sess, environment, summary_writer=None):\n", " \"\"\"The Runner class will expect a function of this type to create an agent.\"\"\"\n", - " return MyRandomDQNAgent(sess, num_actions=environment.action_space.n)\n", + " return MyRandomDQNAgent(num_actions=environment.action_space.n)\n", "\n", "random_dqn_config = \"\"\"\n", "import dopamine.discrete_domains.atari_lib\n", diff --git a/dopamine/colab/cartpole.ipynb b/dopamine/colab/cartpole.ipynb index 067ec5c9..19569529 100644 --- a/dopamine/colab/cartpole.ipynb +++ b/dopamine/colab/cartpole.ipynb @@ -97,42 +97,43 @@ "# @title Load the configuration for DQN.\n", "\n", "DQN_PATH = os.path.join(BASE_PATH, 'dqn')\n", - "# Modified from dopamine/agents/dqn/config/dqn_cartpole.gin\n", + "# Modified from dopamine/jax/agents/dqn/config/dqn_cartpole.gin\n", "dqn_config = \"\"\"\n", "# Hyperparameters for a simple DQN-style Cartpole agent. The hyperparameters\n", "# chosen achieve reasonable performance.\n", "import dopamine.discrete_domains.gym_lib\n", "import dopamine.discrete_domains.run_experiment\n", - "import dopamine.agents.dqn.dqn_agent\n", + "import dopamine.jax.agents.dqn.dqn_agent\n", + "import dopamine.jax.networks\n", "import dopamine.replay_memory.circular_replay_buffer\n", "\n", - "DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", - "DQNAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n", - "DQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", - "DQNAgent.network = @gym_lib.CartpoleDQNNetwork\n", - "DQNAgent.gamma = 0.99\n", - "DQNAgent.update_horizon = 1\n", - "DQNAgent.min_replay_history = 500\n", - "DQNAgent.update_period = 4\n", - "DQNAgent.target_update_period = 100\n", - "DQNAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", - "DQNAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version\n", - "DQNAgent.optimizer = @tf.train.AdamOptimizer()\n", + "JaxDQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", + "JaxDQNAgent.observation_dtype = %jax_networks.CARTPOLE_OBSERVATION_DTYPE\n", + "JaxDQNAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", + "JaxDQNAgent.network = @networks.ClassicControlDQNNetwork\n", + "JaxDQNAgent.gamma = 0.99\n", + "JaxDQNAgent.update_horizon = 1\n", + "JaxDQNAgent.min_replay_history = 500\n", + "JaxDQNAgent.update_period = 4\n", + "JaxDQNAgent.target_update_period = 100\n", + "JaxDQNAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", + "JaxDQNAgent.optimizer = 'adam'\n", "\n", - "tf.train.AdamOptimizer.learning_rate = 0.001\n", - "tf.train.AdamOptimizer.epsilon = 0.0003125\n", + "ClassicControlDQNNetwork.min_vals = %jax_networks.CARTPOLE_MIN_VALS\n", + "ClassicControlDQNNetwork.max_vals = %jax_networks.CARTPOLE_MAX_VALS\n", "\n", "create_gym_environment.environment_name = 'CartPole'\n", "create_gym_environment.version = 'v0'\n", - "create_agent.agent_name = 'dqn'\n", + "create_runner.schedule = 'continuous_train'\n", + "create_agent.agent_name = 'jax_dqn'\n", "TrainRunner.create_environment_fn = @gym_lib.create_gym_environment\n", "Runner.num_iterations = 50\n", "Runner.training_steps = 1000\n", "Runner.evaluation_steps = 1000\n", "Runner.max_steps_per_episode = 200 # Default max episode length.\n", "\n", - "WrappedReplayBuffer.replay_capacity = 50000\n", - "WrappedReplayBuffer.batch_size = 128\n", + "ReplayBuffer.max_capacity = 50_000\n", + "ReplayBuffer.batch_size = 128\n", "\"\"\"\n", "gin.parse_config(dqn_config, skip_unknown=False)" ] @@ -179,46 +180,51 @@ "# @title Load the configuration for C51.\n", "\n", "C51_PATH = os.path.join(BASE_PATH, 'c51')\n", - "# Modified from dopamine/agents/rainbow/config/c51_cartpole.gin\n", + "# Modified from dopamine/jax/agents/rainbow/config/c51_cartpole.gin\n", "c51_config = \"\"\"\n", "# Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters\n", "# chosen achieve reasonable performance.\n", - "import dopamine.agents.dqn.dqn_agent\n", - "import dopamine.agents.rainbow.rainbow_agent\n", + "import dopamine.jax.agents.dqn.dqn_agent\n", + "import dopamine.jax.agents.rainbow.rainbow_agent\n", "import dopamine.discrete_domains.gym_lib\n", "import dopamine.discrete_domains.run_experiment\n", + "import dopamine.jax.networks\n", "import dopamine.replay_memory.prioritized_replay_buffer\n", "\n", - "RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", - "RainbowAgent.observation_dtype = %gym_lib.CARTPOLE_OBSERVATION_DTYPE\n", - "RainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", - "RainbowAgent.network = @gym_lib.CartpoleRainbowNetwork\n", - "RainbowAgent.num_atoms = 51\n", - "RainbowAgent.vmax = 10.\n", - "RainbowAgent.gamma = 0.99\n", - "RainbowAgent.update_horizon = 1\n", - "RainbowAgent.min_replay_history = 500\n", - "RainbowAgent.update_period = 4\n", - "RainbowAgent.target_update_period = 100\n", - "RainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", - "RainbowAgent.replay_scheme = 'uniform'\n", - "RainbowAgent.tf_device = '/gpu:0' # use '/cpu:*' for non-GPU version\n", - "RainbowAgent.optimizer = @tf.train.AdamOptimizer()\n", + "JaxRainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE\n", + "JaxRainbowAgent.observation_dtype = %jax_networks.CARTPOLE_OBSERVATION_DTYPE\n", + "JaxRainbowAgent.stack_size = %gym_lib.CARTPOLE_STACK_SIZE\n", + "JaxRainbowAgent.network = @networks.ClassicControlRainbowNetwork\n", + "JaxRainbowAgent.num_atoms = 201\n", + "JaxRainbowAgent.vmax = 100.\n", + "JaxRainbowAgent.gamma = 0.99\n", + "JaxRainbowAgent.epsilon_eval = 0.\n", + "JaxRainbowAgent.epsilon_train = 0.01\n", + "JaxRainbowAgent.update_horizon = 1\n", + "JaxRainbowAgent.min_replay_history = 500\n", + "JaxRainbowAgent.update_period = 1\n", + "JaxRainbowAgent.target_update_period = 1\n", + "JaxRainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon\n", + "JaxRainbowAgent.replay_scheme = 'uniform'\n", + "create_optimizer.learning_rate = 0.00001\n", + "create_optimizer.eps = 0.00000390625\n", "\n", - "tf.train.AdamOptimizer.learning_rate = 0.001\n", - "tf.train.AdamOptimizer.epsilon = 0.0003125\n", + "ClassicControlRainbowNetwork.min_vals = %jax_networks.CARTPOLE_MIN_VALS\n", + "ClassicControlRainbowNetwork.max_vals = %jax_networks.CARTPOLE_MAX_VALS\n", "\n", "create_gym_environment.environment_name = 'CartPole'\n", "create_gym_environment.version = 'v0'\n", - "create_agent.agent_name = 'rainbow'\n", + "create_runner.schedule = 'continuous_train'\n", + "create_agent.agent_name = 'jax_rainbow'\n", "TrainRunner.create_environment_fn = @gym_lib.create_gym_environment\n", "Runner.num_iterations = 50\n", "Runner.training_steps = 1000\n", "Runner.evaluation_steps = 1000\n", "Runner.max_steps_per_episode = 200 # Default max episode length.\n", "\n", - "WrappedPrioritizedReplayBuffer.replay_capacity = 50000\n", - "WrappedPrioritizedReplayBuffer.batch_size = 128\n", + "ReplayBuffer.max_capacity = 50_000\n", + "ReplayBuffer.batch_size = 128\n", + "PrioritizedSamplingDistribution.max_capacity = 50_000\n", "\"\"\"\n", "gin.parse_config(c51_config, skip_unknown=False)" ] diff --git a/dopamine/colab/load_statistics.ipynb b/dopamine/colab/load_statistics.ipynb index f8bb00c4..fd006722 100644 --- a/dopamine/colab/load_statistics.ipynb +++ b/dopamine/colab/load_statistics.ipynb @@ -64,7 +64,7 @@ "\n", "import numpy as np\n", "import os\n", - "from dopamine.agents.dqn import dqn_agent\n", + "from dopamine.tf.agents.dqn import dqn_agent\n", "from dopamine.discrete_domains import run_experiment\n", "from dopamine.colab import utils as colab_utils\n", "from absl import flags\n", diff --git a/dopamine/continuous_domains/run_experiment.py b/dopamine/continuous_domains/run_experiment.py index 6365fae1..dd239d52 100644 --- a/dopamine/continuous_domains/run_experiment.py +++ b/dopamine/continuous_domains/run_experiment.py @@ -21,7 +21,10 @@ from dopamine.discrete_domains import iteration_statistics from dopamine.discrete_domains import run_experiment as base_run_experiment from dopamine.jax.agents.dqn import dqn_agent +from dopamine.jax.agents.ppo import ppo_agent from dopamine.jax.agents.sac import sac_agent +from dopamine.labs.cale import ppo_cale +from dopamine.labs.cale import sac_cale # pylint: disable=unused-import from dopamine.labs.sac_from_pixels import deepmind_control_lib # pylint: enable=unused-import @@ -58,9 +61,29 @@ def create_continuous_agent( """ assert agent_name is not None if agent_name == 'sac': + assert isinstance(environment.action_space, spaces.box.Box) + assert isinstance(environment.observation_space, spaces.box.Box) + return sac_agent.SACAgent( + action_shape=environment.action_space.shape, + action_limits=( + environment.action_space.low, + environment.action_space.high, + ), + observation_shape=environment.observation_space.shape, + action_dtype=environment.action_space.dtype, + observation_dtype=environment.observation_space.dtype, + summary_writer=summary_writer, + ) + elif agent_name == 'ppo': assert isinstance(environment.action_space, spaces.Box) assert isinstance(environment.observation_space, spaces.Box) - return sac_agent.SACAgent( + return ppo_agent.PPOAgent( + action_shape=environment.action_space.shape, + observation_shape=environment.observation_space.shape, + summary_writer=summary_writer, + ) + elif agent_name.startswith('sac_cale'): + return sac_cale.SACCALEAgent( action_shape=environment.action_space.shape, action_limits=( environment.action_space.low, @@ -71,6 +94,16 @@ def create_continuous_agent( observation_dtype=environment.observation_space.dtype, summary_writer=summary_writer, ) + elif agent_name.startswith('ppo_cale'): + return ppo_cale.PPOCALEAgent( + action_shape=environment.action_space.shape, + action_limits=( + environment.action_space.low, + environment.action_space.high, + ), + observation_shape=environment.observation_space.shape, + summary_writer=summary_writer, + ) else: raise ValueError(f'Unknown agent: {agent_name}') @@ -306,3 +339,5 @@ def _save_tensorboard_summaries( for name, value in metrics: self._summary_writer.scalar(name, value, iteration) self._summary_writer.flush() + + diff --git a/dopamine/discrete_domains/atari_lib.py b/dopamine/discrete_domains/atari_lib.py index 293ceb0e..69712b2f 100644 --- a/dopamine/discrete_domains/atari_lib.py +++ b/dopamine/discrete_domains/atari_lib.py @@ -36,19 +36,18 @@ class has two main functions: `.__init__` and `.call`. When we create our used. Please use the appropriate signature as needed when defining new networks. """ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - import collections import math from absl import logging +from ale_py import registration +from baselines.common import atari_wrappers import cv2 import gin -import gym -from gym.spaces.box import Box +import gym as legacy_gym +import gymnasium as gym +from gymnasium.spaces.box import Box import numpy as np import tensorflow as tf @@ -69,7 +68,13 @@ class has two main functions: `.__init__` and `.call`. When we create our @gin.configurable -def create_atari_environment(game_name=None, sticky_actions=True): +def create_atari_environment( + game_name=None, + sticky_actions=True, + use_legacy_gym=False, + use_ppo_preprocessing=False, + continuous_action_threshold=None, +): """Wraps an Atari 2600 Gym environment with some basic preprocessing. This preprocessing matches the guidelines proposed in Machado et al. (2017), @@ -88,20 +93,59 @@ def create_atari_environment(game_name=None, sticky_actions=True): Args: game_name: str, the name of the Atari 2600 domain. sticky_actions: bool, whether to use sticky_actions as per Machado et al. + use_legacy_gym: bool, whether to use use the legacy Gym API. + use_ppo_preprocessing: bool, whether to use preprocessing for PPO. + continuous_action_threshold: Optional[float], if not None, will use CALE + (the continuous version of the ALE) with this action threshold. Returns: An Atari 2600 environment with some standard preprocessing. """ assert game_name is not None - game_version = 'v0' if sticky_actions else 'v4' - full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version) - env = gym.make(full_game_name) - # Strip out the TimeLimit wrapper from Gym, which caps us at 100k frames. We - # handle this time limit internally instead, which lets us cap at 108k frames - # (30 minutes). The TimeLimit wrapper also plays poorly with saving and - # restoring states. - env = env.env - env = AtariPreprocessing(env) + if use_legacy_gym: + copy_roms(roms_dir) + game_version = 'v0' if sticky_actions else 'v4' + full_game_name = f'{game_name}NoFrameskip-{game_version}' + env = legacy_gym.make(full_game_name) + else: + registration.register_v5_envs() + registration.register_v5_envs() + full_game_name = f'ALE/{game_name}-v5' + repeat_action_probability = 0.25 if sticky_actions else 0.0 + continuous = continuous_action_threshold is not None + continuous_action_threshold = ( + 0.0 + if continuous_action_threshold is None + else continuous_action_threshold + ) + env = gym.make( + full_game_name, + repeat_action_probability=repeat_action_probability, + frameskip=1, + max_num_frames_per_episode=100_000, + continuous=continuous, + continuous_action_threshold=continuous_action_threshold, + ) + + if use_ppo_preprocessing: + env = atari_wrappers.NoopResetEnv(env, noop_max=30) + env = atari_wrappers.MaxAndSkipEnv(env, skip=4) + env = atari_wrappers.EpisodicLifeEnv(env) + if 'FIRE' in env.unwrapped.get_action_meanings(): + env = atari_wrappers.FireResetEnv(env) + env = atari_wrappers.ClipRewardEnv(env) + env = gym.wrappers.ResizeObservation(env, (84, 84)) + env = gym.wrappers.GrayScaleObservation(env) + env = gym.wrappers.FrameStack(env, 4) + env = env.env # Strip the TimeLimit wrapper + env = GameOverWrapper(env) + else: + # Strip out the TimeLimit wrapper from Gym, which caps us at 100k frames. We + # handle this time limit internally instead, which lets us cap at 108k + # frames (30 minutes). The TimeLimit wrapper also plays poorly with saving + # and restoring states. + env = env.env + env = AtariPreprocessing(env) return env @@ -435,6 +479,7 @@ def __init__( frame_skip=4, terminal_on_life_loss=False, screen_size=84, + use_legacy_gym=False, ): """Constructor for an Atari 2600 preprocessor. @@ -444,25 +489,25 @@ def __init__( terminal_on_life_loss: bool, If True, the step() method returns is_terminal=True whenever a life is lost. See Mnih et al. 2015. screen_size: int, size of a resized Atari 2600 frame. + use_legacy_gym: bool, whether to use legacy Gym or new Gymnasium API. Raises: ValueError: if frame_skip or screen_size are not strictly positive. """ if frame_skip <= 0: raise ValueError( - 'Frame skip should be strictly positive, got {}'.format(frame_skip) + f'Frame skip should be strictly positive, got {frame_skip}' ) if screen_size <= 0: raise ValueError( - 'Target screen size should be strictly positive, got {}'.format( - screen_size - ) + f'Target screen size should be strictly positive, got {screen_size}' ) self.environment = environment self.terminal_on_life_loss = terminal_on_life_loss self.frame_skip = frame_skip self.screen_size = screen_size + self._use_legacy_gym = use_legacy_gym obs_dims = self.environment.observation_space # Stores temporary observations used for pooling over two successive @@ -556,7 +601,11 @@ def step(self, action): for time_step in range(self.frame_skip): # We bypass the Gym observation altogether and directly fetch the # grayscale image from the ALE. This is a little faster. - _, reward, game_over, info = self.environment.step(action) + if self._use_legacy_gym: + _, reward, game_over, info = self.environment.step(action) + else: + _, reward, terminated, truncated, info = self.environment.step(action) + game_over = terminated or truncated accumulated_reward += reward if self.terminal_on_life_loss: @@ -617,3 +666,29 @@ def _pool_and_resize(self): ) int_image = np.asarray(transformed_image, dtype=np.uint8) return np.expand_dims(int_image, axis=2) + + +@gin.configurable +class GameOverWrapper(object): + """A Wrapper class around Gym environments adding game over signal.""" + + def __init__(self, environment): + self.environment = environment + self.game_over = False + + @property + def observation_space(self): + return self.environment.observation_space + + @property + def action_space(self): + return self.environment.action_space + + def reset(self): + return self.environment.reset() + + def step(self, action): + observation, reward, game_over, info = self.environment.step(action) + truncated = info.get('TimeLimit.truncated', False) + self.game_over = game_over and not truncated + return observation, reward, self.game_over, info diff --git a/dopamine/discrete_domains/gym_lib.py b/dopamine/discrete_domains/gym_lib.py index 1f5b5c26..2bdb2ca7 100644 --- a/dopamine/discrete_domains/gym_lib.py +++ b/dopamine/discrete_domains/gym_lib.py @@ -21,19 +21,14 @@ environments conformant with the API Dopamine is expecting. """ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - import itertools import math - - from dopamine.discrete_domains import atari_lib import gin -import gym +import gym as legacy_gym from gym.wrappers.time_limit import TimeLimit +import gymnasium as gym import numpy as np import tensorflow as tf @@ -62,12 +57,19 @@ @gin.configurable -def create_gym_environment(environment_name=None, version='v0'): +def create_gym_environment( + environment_name=None, + version='v0', + use_legacy_gym=False, + use_ppo_preprocessing=False, +): """Wraps a Gym environment with some basic preprocessing. Args: environment_name: str, the name of the environment to run. version: str, version of the environment to run. + use_legacy_gym: bool, whether to use the legacy Gym API. + use_ppo_preprocessing: bool, whether to use PPO-specific preprocessing. Returns: A Gym environment with some standard preprocessing. @@ -76,13 +78,26 @@ def create_gym_environment(environment_name=None, version='v0'): full_game_name = '{}-{}'.format(environment_name, version) - env = gym.make(full_game_name) + if use_legacy_gym: + env = legacy_gym.make(full_game_name) + if use_ppo_preprocessing: + env = legacy_gym.wrappers.ClipAction(env) + env = legacy_gym.wrappers.NormalizeObservation(env) + env = legacy_gym.wrappers.TransformObservation( + env, lambda obs: np.clip(obs, -10, 10) + ) + env = legacy_gym.wrappers.NormalizeReward(env) + env = legacy_gym.wrappers.TransformReward( + env, lambda reward: np.clip(reward, -10, 10) + ) + else: + env = gym.make(full_game_name) # Strip out the TimeLimit wrapper from Gym, which caps us at 200 steps. if isinstance(env, TimeLimit): env = env.env # Wrap the returned environment in a class which conforms to the API expected # by Dopamine. - env = GymPreprocessing(env) + env = GymPreprocessing(env, use_legacy_gym=use_legacy_gym) return env @@ -432,8 +447,9 @@ def call(self, state): class GymPreprocessing(object): """A Wrapper class around Gym environments.""" - def __init__(self, environment): + def __init__(self, environment, use_legacy_gym=False): self.environment = environment + self._use_legacy_gym = use_legacy_gym self.game_over = False @property @@ -453,11 +469,19 @@ def metadata(self): return self.environment.metadata def reset(self): - return self.environment.reset() + if self._use_legacy_gym: + return self.environment.reset() + + obs, _ = self.environment.reset() + return obs def step(self, action): - observation, reward, game_over, info = self.environment.step(action) - was_truncated = info.get('TimeLimit.truncated', False) - game_over = game_over and not was_truncated - self.game_over = game_over - return observation, reward, game_over, info + if self._use_legacy_gym: + observation, reward, game_over, info = self.environment.step(action) + truncated = info.get('TimeLimit.truncated', False) + else: + observation, reward, game_over, truncated, info = self.environment.step( + action + ) + self.game_over = game_over and not truncated + return observation, reward, self.game_over, info diff --git a/dopamine/discrete_domains/run_experiment.py b/dopamine/discrete_domains/run_experiment.py index dbc76cad..9ee29bdd 100644 --- a/dopamine/discrete_domains/run_experiment.py +++ b/dopamine/discrete_domains/run_experiment.py @@ -23,9 +23,6 @@ import time from absl import logging -from dopamine.agents.dqn import dqn_agent -from dopamine.agents.implicit_quantile import implicit_quantile_agent -from dopamine.agents.rainbow import rainbow_agent from dopamine.discrete_domains import atari_lib from dopamine.discrete_domains import checkpointer from dopamine.discrete_domains import iteration_statistics @@ -33,6 +30,7 @@ from dopamine.jax.agents.dqn import dqn_agent as jax_dqn_agent from dopamine.jax.agents.full_rainbow import full_rainbow_agent from dopamine.jax.agents.implicit_quantile import implicit_quantile_agent as jax_implicit_quantile_agent +from dopamine.jax.agents.ppo import ppo_agent from dopamine.jax.agents.quantile import quantile_agent as jax_quantile_agent from dopamine.jax.agents.rainbow import rainbow_agent as jax_rainbow_agent from dopamine.labs.moes.agents import dqn_moe_agent @@ -40,6 +38,9 @@ from dopamine.labs.moes.agents import rainbow_100k_moe_agent from dopamine.metrics import collector_dispatcher from dopamine.metrics import statistics_instance +from dopamine.tf.agents.dqn import dqn_agent +from dopamine.tf.agents.implicit_quantile import implicit_quantile_agent +from dopamine.tf.agents.rainbow import rainbow_agent import gin import numpy as np import tensorflow as tf @@ -135,6 +136,13 @@ def create_agent( return rainbow_100k_moe_agent.Atari100kRainbowMoEAgent( num_actions=environment.action_space.n, summary_writer=summary_writer ) + elif agent_name == 'ppo': + return ppo_agent.PPOAgent( + action_shape=environment.action_space.n, + observation_shape=dqn_agent.NATURE_DQN_OBSERVATION_SHAPE, + stack_size=dqn_agent.NATURE_DQN_STACK_SIZE, + summary_writer=summary_writer, + ) else: raise ValueError('Unknown agent: {}'.format(agent_name)) @@ -162,6 +170,7 @@ def create_runner(base_dir, schedule='continuous_train_and_eval'): return TrainRunner(base_dir, create_agent) else: raise ValueError('Unknown schedule: {}'.format(schedule)) + logging.info('schedule: %s', schedule) @gin.configurable @@ -216,7 +225,7 @@ def __init__( training_steps: int, the number of training steps to perform. evaluation_steps: int, the number of evaluation steps to perform. max_steps_per_episode: int, maximum number of steps after which an episode - terminates. + terminates. Set to None to continue episodes between iterations. clip_rewards: bool, whether to clip rewards in [-1, 1]. use_legacy_logger: bool, whether to use the legacy Logger. This will be deprecated soon, replaced with the new CollectorDispatcher setup. @@ -245,6 +254,17 @@ def __init__( self._clip_rewards = clip_rewards self._create_directories() + logging.info('log_every_n: %d', self._log_every_n) + logging.info('num_iterations: %d', self._num_iterations) + logging.info('training_steps: %d', self._training_steps) + logging.info('evaluation_steps: %d', self._evaluation_steps) + if self._max_steps_per_episode is not None: + logging.info('max_steps_per_episode: %d', self._max_steps_per_episode) + else: + logging.info('max_steps_per_episode: None') + logging.info('base_dir: %s', self._base_dir) + logging.info('clip_rewards: %s', self._clip_rewards) + self._environment = create_environment_fn() # The agent is now in charge of setting up the session. self._sess = None @@ -392,7 +412,6 @@ def _run_one_episode(self): total_reward = 0.0 action = self._initialize_episode() - is_terminal = False # Keep interacting until we reach a terminal state. while True: @@ -423,6 +442,46 @@ def _run_one_episode(self): return step_number, total_reward + def _run_continued_episode(self, start_step_count, max_step_count): + """Executes a full trajectory of the agent interacting with the environment. + + Args: + start_step_count: int, the number of steps taken so far in current phase. + max_step_count: int, the maximum number of steps to take in this phase. + + Returns: + The number of steps taken and the total reward. + """ + step_number = 0 + total_reward = 0.0 + if not hasattr(self, 'episode_ended') or self.episode_ended: + self.action = self._initialize_episode() + self.episode_ended = False + + while start_step_count + step_number <= max_step_count: + observation, reward, is_terminal = self._run_one_step(self.action) + + total_reward += reward + step_number += 1 + + if self._clip_rewards: + # Perform reward clipping. + reward = np.clip(reward, -1, 1) + + if self._environment.game_over: + self._end_episode(reward, is_terminal) + self.episode_ended = True + break + elif is_terminal: + # If we lose a life but the episode is not over, signal an artificial + # end of episode to the agent. + self._end_episode(reward, is_terminal) + self.action = self._agent.begin_episode(observation) + else: + self.action = self._agent.step(reward, observation) + + return step_number, total_reward + def _run_one_phase(self, min_steps, statistics, run_mode_str): """Runs the agent/environment loop until a desired number of steps. @@ -444,7 +503,12 @@ def _run_one_phase(self, min_steps, statistics, run_mode_str): sum_returns = 0.0 while step_count < min_steps: - episode_length, episode_return = self._run_one_episode() + if self._max_steps_per_episode is None: + episode_length, episode_return = self._run_continued_episode( + step_count, min_steps + ) + else: + episode_length, episode_return = self._run_one_episode() statistics.append({ '{}_episode_lengths'.format(run_mode_str): episode_length, '{}_episode_returns'.format(run_mode_str): episode_return, @@ -794,3 +858,5 @@ def _save_tensorboard_summaries( ] ) self._summary_writer.add_summary(summary, iteration) + + diff --git a/dopamine/jax/agents/dqn/configs/dqn.gin b/dopamine/jax/agents/dqn/configs/dqn.gin index 17a17ae0..0263076f 100644 --- a/dopamine/jax/agents/dqn/configs/dqn.gin +++ b/dopamine/jax/agents/dqn/configs/dqn.gin @@ -4,16 +4,16 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.jax.replay_memory.replay_buffer JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 1 -JaxDQNAgent.min_replay_history = 20000 # agent steps +JaxDQNAgent.min_replay_history = 20_000 # agent steps JaxDQNAgent.update_period = 4 -JaxDQNAgent.target_update_period = 8000 # agent steps +JaxDQNAgent.target_update_period = 8_000 # agent steps JaxDQNAgent.epsilon_train = 0.01 JaxDQNAgent.epsilon_eval = 0.001 -JaxDQNAgent.epsilon_decay_period = 250000 # agent steps +JaxDQNAgent.epsilon_decay_period = 250_000 # agent steps # Note: We are using the Adam optimizer by default for JaxDQN, which differs # from the original NatureDQN and the dopamine TensorFlow version. In # the experiments we have ran, we have found that using Adam yields @@ -29,9 +29,9 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps +Runner.training_steps = 250_000 # agent steps +Runner.evaluation_steps = 125_000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphReplayBuffer.replay_capacity = 1000000 -OutOfGraphReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 diff --git a/dopamine/jax/agents/dqn/configs/dqn_acrobot.gin b/dopamine/jax/agents/dqn/configs/dqn_acrobot.gin index fd006caf..d6bcb09d 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_acrobot.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_acrobot.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE @@ -31,9 +30,9 @@ create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 500 -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_cartpole.gin b/dopamine/jax/agents/dqn/configs/dqn_cartpole.gin index 8128c9e0..115d9e32 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_cartpole.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_cartpole.gin @@ -3,8 +3,8 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent +import dopamine.jax.replay_memory.replay_buffer import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE @@ -30,10 +30,10 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment -Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.num_iterations = 100 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 200 # Default max episode length. -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_fourier_acrobot.gin b/dopamine/jax/agents/dqn/configs/dqn_fourier_acrobot.gin index db09e40f..7774b858 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_fourier_acrobot.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_fourier_acrobot.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE @@ -31,9 +30,9 @@ create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 500 -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_fourier_cartpole.gin b/dopamine/jax/agents/dqn/configs/dqn_fourier_cartpole.gin index 22576b1c..b6cb869d 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_fourier_cartpole.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_fourier_cartpole.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE @@ -31,9 +30,9 @@ create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 200 # Default max episode length. -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_fourier_lunarlander.gin b/dopamine/jax/agents/dqn/configs/dqn_fourier_lunarlander.gin index e9f66074..49a507c4 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_fourier_lunarlander.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_fourier_lunarlander.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.LUNAR_OBSERVATION_SHAPE @@ -28,9 +27,9 @@ create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 125 -Runner.training_steps = 4000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 1000 +Runner.training_steps = 4_000 +Runner.evaluation_steps = 1_000 +Runner.max_steps_per_episode = 1_000 -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_fourier_mountaincar.gin b/dopamine/jax/agents/dqn/configs/dqn_fourier_mountaincar.gin index 186b5c8a..309b93be 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_fourier_mountaincar.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_fourier_mountaincar.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.MOUNTAINCAR_OBSERVATION_SHAPE @@ -31,8 +30,8 @@ create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 +Runner.training_steps = 1_000 Runner.max_steps_per_episode = 600 # Default max episode length. -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_lunarlander.gin b/dopamine/jax/agents/dqn/configs/dqn_lunarlander.gin index 3bab61e8..6d380719 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_lunarlander.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_lunarlander.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.LUNAR_OBSERVATION_SHAPE @@ -28,9 +27,9 @@ create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 125 -Runner.training_steps = 4000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 1000 +Runner.training_steps = 4_000 +Runner.evaluation_steps = 1_000 +Runner.max_steps_per_episode = 1_000 -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_mountaincar.gin b/dopamine/jax/agents/dqn/configs/dqn_mountaincar.gin index ba033f66..40f62963 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_mountaincar.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_mountaincar.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %gym_lib.MOUNTAINCAR_OBSERVATION_SHAPE @@ -31,8 +30,8 @@ create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 +Runner.training_steps = 1_000 Runner.max_steps_per_episode = 600 # Default max episode length. -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 diff --git a/dopamine/jax/agents/dqn/configs/dqn_profiling.gin b/dopamine/jax/agents/dqn/configs/dqn_profiling.gin index 5834c3d6..3e53e9b2 100644 --- a/dopamine/jax/agents/dqn/configs/dqn_profiling.gin +++ b/dopamine/jax/agents/dqn/configs/dqn_profiling.gin @@ -4,13 +4,12 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 1 JaxDQNAgent.min_replay_history = 100 # agent steps JaxDQNAgent.update_period = 4 -JaxDQNAgent.target_update_period = 8000 # agent steps +JaxDQNAgent.target_update_period = 8_000 # agent steps JaxDQNAgent.epsilon_fn = @dqn_agent.identity_epsilon JaxDQNAgent.epsilon_train = 0.0 JaxDQNAgent.epsilon_eval = 0.0 @@ -22,9 +21,9 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_dqn' create_agent.debug_mode = True Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps +Runner.training_steps = 250_000 # agent steps +Runner.evaluation_steps = 125_000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphReplayBuffer.replay_capacity = 1000000 -OutOfGraphReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 diff --git a/dopamine/jax/agents/dqn/dqn_agent.py b/dopamine/jax/agents/dqn/dqn_agent.py index 9df7df20..794e1c59 100644 --- a/dopamine/jax/agents/dqn/dqn_agent.py +++ b/dopamine/jax/agents/dqn/dqn_agent.py @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Compact implementation of a DQN agent in JAx.""" +"""Compact implementation of a DQN agent in Jax.""" import collections import functools @@ -21,12 +21,14 @@ import time from absl import logging -from dopamine.agents.dqn import dqn_agent +from dopamine.discrete_domains import atari_lib from dopamine.jax import losses from dopamine.jax import networks +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers from dopamine.metrics import statistics_instance -from dopamine.replay_memory import circular_replay_buffer -from dopamine.replay_memory import prioritized_replay_buffer import gin import jax import jax.numpy as jnp @@ -35,10 +37,16 @@ import tensorflow as tf -NATURE_DQN_OBSERVATION_SHAPE = dqn_agent.NATURE_DQN_OBSERVATION_SHAPE +NATURE_DQN_OBSERVATION_SHAPE = atari_lib.NATURE_DQN_OBSERVATION_SHAPE NATURE_DQN_DTYPE = jnp.uint8 -NATURE_DQN_STACK_SIZE = dqn_agent.NATURE_DQN_STACK_SIZE -identity_epsilon = dqn_agent.identity_epsilon +NATURE_DQN_STACK_SIZE = atari_lib.NATURE_DQN_STACK_SIZE + + +@gin.configurable +def identity_epsilon( + unused_decay_period, unused_step, unused_warmup_steps, epsilon +): + return epsilon @gin.configurable @@ -49,6 +57,8 @@ def create_optimizer( beta2=0.999, eps=1.5e-4, centered=False, + anneal_learning_rate=False, + anneal_steps=1_000_000, ): """Create an optimizer for training. @@ -61,15 +71,20 @@ def create_optimizer( beta2: float, beta2 parameter for the optimizer. eps: float, epsilon parameter for the optimizer. centered: bool, centered parameter for RMSProp. + anneal_learning_rate: bool, whether to anneal the learning rate. + anneal_steps: int, number of steps to anneal the learning rate over. Returns: An optax optimizer. """ + learning_rate_value = learning_rate + if anneal_learning_rate: + learning_rate = optax.linear_schedule(learning_rate, 0.0, anneal_steps) if name == 'adam': logging.info( 'Creating Adam optimizer with settings lr=%f, beta1=%f, ' 'beta2=%f, eps=%f', - learning_rate, + learning_rate_value, beta1, beta2, eps, @@ -78,13 +93,15 @@ def create_optimizer( elif name == 'rmsprop': logging.info( 'Creating RMSProp optimizer with settings lr=%f, beta2=%f, eps=%f', - learning_rate, + learning_rate_value, beta2, eps, ) return optax.rmsprop(learning_rate, decay=beta2, eps=eps, centered=centered) elif name == 'sgd': - logging.info('Creating SGD optimizer with settings lr=%f', learning_rate) + logging.info( + 'Creating SGD optimizer with settings lr=%f', learning_rate_value + ) return optax.sgd(learning_rate) else: raise ValueError('Unsupported optimizer {}'.format(name)) @@ -310,7 +327,7 @@ def __init__( be used to specify which Collectors to log to. """ assert isinstance(observation_shape, tuple) - seed = int(time.time() * 1e6) if seed is None else seed + self._seed = int(time.time() * 1e6) if seed is None else seed logging.info( 'Creating %s agent with the following parameters:', self.__class__.__name__, @@ -324,7 +341,7 @@ def __init__( logging.info('\t epsilon_eval: %f', epsilon_eval) logging.info('\t epsilon_decay_period: %d', epsilon_decay_period) logging.info('\t optimizer: %s', optimizer) - logging.info('\t seed: %d', seed) + logging.info('\t seed: %d', self._seed) logging.info('\t loss_type: %s', loss_type) logging.info('\t preprocess_fn: %s', preprocess_fn) logging.info('\t summary_writing_frequency: %d', summary_writing_frequency) @@ -366,8 +383,9 @@ def __init__( self.allow_partial_reload = allow_partial_reload self._loss_type = loss_type self._collector_allowlist = collector_allowlist + self._replay_scheme = 'uniform' - self._rng = jax.random.PRNGKey(seed) + self._rng = jax.random.PRNGKey(self._seed) state_shape = self.observation_shape + (stack_size,) self.state = onp.zeros(state_shape) self._replay = self._build_replay_buffer() @@ -389,20 +407,31 @@ def _build_networks_and_optimizer(self): def _build_replay_buffer(self): """Creates the replay buffer used by the agent.""" - return circular_replay_buffer.OutOfGraphReplayBuffer( - observation_shape=self.observation_shape, + transition_accumulator = accumulator.TransitionAccumulator( stack_size=self.stack_size, update_horizon=self.update_horizon, gamma=self.gamma, - observation_dtype=self.observation_dtype, + ) + sampling_distribution = samplers.UniformSamplingDistribution( + seed=self._seed + ) + return replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=sampling_distribution, ) def _sample_from_replay_buffer(self): - samples = self._replay.sample_transition_batch() - types = self._replay.get_transition_elements() + """Sample elements from the replay buffer.""" self.replay_elements = collections.OrderedDict() - for element, element_type in zip(samples, types): - self.replay_elements[element_type.name] = element + elems, metadata = self._replay.sample(with_sample_metadata=True) + self.replay_elements['state'] = elems.state + self.replay_elements['next_state'] = elems.next_state + self.replay_elements['action'] = elems.action + self.replay_elements['reward'] = elems.reward + self.replay_elements['terminal'] = elems.is_terminal + if self._replay_scheme == 'prioritized': + self.replay_elements['indices'] = metadata.keys + self.replay_elements['sampling_probabilities'] = metadata.probabilities def _sync_weights(self): """Syncs the target_network_params with online_params.""" @@ -602,25 +631,31 @@ def _store_transition( This can be different than terminal when ending the episode because of a timeout, for example. """ + # pylint: disable=protected-access is_prioritized = isinstance( - self._replay, - prioritized_replay_buffer.OutOfGraphPrioritizedReplayBuffer, + self._replay._sampling_distribution, + samplers.PrioritizedSamplingDistribution, ) if is_prioritized and priority is None: if self._replay_scheme == 'uniform': priority = 1.0 else: - priority = self._replay.sum_tree.max_recorded_priority + priority = ( + self._replay._sampling_distribution._sum_tree.max_recorded_priority + ) + # pylint: enable=protected-access if not self.eval_mode: self._replay.add( - last_observation, - action, - reward, - is_terminal, - *args, + elements.TransitionElement( + last_observation, + action, + reward, + is_terminal, + episode_end, + ), priority=priority, - episode_end=episode_end + *args, ) def bundle_and_checkpoint(self, checkpoint_dir, iteration_number): @@ -674,7 +709,7 @@ def unbundle(self, checkpoint_dir, iteration_number, bundle_dictionary): # self._replay.load() will throw a NotFoundError if it does not find all # the necessary files. self._replay.load(checkpoint_dir, iteration_number) - except tf.errors.NotFoundError: + except (tf.errors.NotFoundError, FileNotFoundError): if not self.allow_partial_reload: # If we don't allow partial reloads, we will return False. return False diff --git a/dopamine/jax/agents/full_rainbow/configs/full_rainbow.gin b/dopamine/jax/agents/full_rainbow/configs/full_rainbow.gin index 5f6b2ef7..b825cee7 100644 --- a/dopamine/jax/agents/full_rainbow/configs/full_rainbow.gin +++ b/dopamine/jax/agents/full_rainbow/configs/full_rainbow.gin @@ -5,16 +5,15 @@ import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 3 -JaxDQNAgent.min_replay_history = 20000 # agent steps +JaxDQNAgent.min_replay_history = 20_000 # agent steps JaxDQNAgent.update_period = 4 -JaxDQNAgent.target_update_period = 8000 # agent steps +JaxDQNAgent.target_update_period = 8_000 # agent steps JaxDQNAgent.epsilon_train = 0.01 JaxDQNAgent.epsilon_eval = 0.001 -JaxDQNAgent.epsilon_decay_period = 250000 # agent steps +JaxDQNAgent.epsilon_decay_period = 250_000 # agent steps JaxDQNAgent.optimizer = 'adam' JaxFullRainbowAgent.noisy = True @@ -35,9 +34,10 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'full_rainbow' create_agent.debug_mode = True Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps +Runner.training_steps = 250_000 # agent steps +Runner.evaluation_steps = 125_000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/full_rainbow/configs/full_rainbow_profiling.gin b/dopamine/jax/agents/full_rainbow/configs/full_rainbow_profiling.gin index fc002491..8a1db39b 100644 --- a/dopamine/jax/agents/full_rainbow/configs/full_rainbow_profiling.gin +++ b/dopamine/jax/agents/full_rainbow/configs/full_rainbow_profiling.gin @@ -5,16 +5,15 @@ import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 3 JaxDQNAgent.min_replay_history = 100 # agent steps JaxDQNAgent.update_period = 4 -JaxDQNAgent.target_update_period = 8000 # agent steps +JaxDQNAgent.target_update_period = 8_000 # agent steps JaxDQNAgent.epsilon_train = 0.0 JaxDQNAgent.epsilon_eval = 0.0 -JaxDQNAgent.epsilon_decay_period = 250000 # agent steps +JaxDQNAgent.epsilon_decay_period = 250_000 # agent steps JaxDQNAgent.optimizer = 'adam' JaxFullRainbowAgent.num_atoms = 51 @@ -36,9 +35,10 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps +Runner.training_steps = 250_000 # agent steps +Runner.evaluation_steps = 125_000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/full_rainbow/full_rainbow_agent.py b/dopamine/jax/agents/full_rainbow/full_rainbow_agent.py index 25e78dbb..6884dea8 100644 --- a/dopamine/jax/agents/full_rainbow/full_rainbow_agent.py +++ b/dopamine/jax/agents/full_rainbow/full_rainbow_agent.py @@ -34,8 +34,11 @@ from dopamine.jax import networks from dopamine.jax.agents.dqn import dqn_agent from dopamine.jax.agents.rainbow import rainbow_agent +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers from dopamine.metrics import statistics_instance -from dopamine.replay_memory import prioritized_replay_buffer import gin import jax import jax.numpy as jnp @@ -348,12 +351,18 @@ def _build_replay_buffer(self): """Creates the replay buffer used by the agent.""" if self._replay_scheme not in ['uniform', 'prioritized']: raise ValueError('Invalid replay scheme: {}'.format(self._replay_scheme)) - return prioritized_replay_buffer.OutOfGraphPrioritizedReplayBuffer( - observation_shape=self.observation_shape, + + transition_accumulator = accumulator.TransitionAccumulator( stack_size=self.stack_size, update_horizon=self.update_horizon, gamma=self.gamma, - observation_dtype=self.observation_dtype, + ) + sampling_distribution = samplers.PrioritizedSamplingDistribution( + seed=self._seed + ) + return replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=sampling_distribution, ) def _training_step_update(self): @@ -406,8 +415,9 @@ def _training_step_update(self): # small nonzero value to the loss to avoid 0 priority items. While # technically this may be okay, setting all items to 0 priority will # cause troubles, and also result in 1.0 / 0.0 = NaN correction terms. - self._replay.set_priority( - self.replay_elements['indices'], jnp.sqrt(loss + 1e-10) + self._replay.update( + self.replay_elements['indices'], + priorities=jnp.sqrt(loss + 1e-10), ) if ( @@ -441,25 +451,31 @@ def _store_transition( episode_end=False ): """Stores a transition when in training mode.""" + # pylint: disable=protected-access is_prioritized = isinstance( - self._replay, - prioritized_replay_buffer.OutOfGraphPrioritizedReplayBuffer, + self._replay._sampling_distribution, + samplers.PrioritizedSamplingDistribution, ) if is_prioritized and priority is None: if self._replay_scheme == 'uniform': priority = 1.0 else: - priority = self._replay.sum_tree.max_recorded_priority + priority = ( + self._replay._sampling_distribution._sum_tree.max_recorded_priority + ) + # pylint: enable=protected-access if not self.eval_mode: self._replay.add( - last_observation, - action, - reward, - is_terminal, - *args, + elements.TransitionElement( + last_observation, + action, + reward, + is_terminal, + episode_end, + ), priority=priority, - episode_end=episode_end + *args, ) def _train_step(self): diff --git a/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile.gin b/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile.gin index 612cb247..54fba364 100644 --- a/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile.gin +++ b/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile.gin @@ -4,7 +4,6 @@ import dopamine.jax.agents.implicit_quantile.implicit_quantile_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.circular_replay_buffer JaxImplicitQuantileAgent.kappa = 1.0 JaxImplicitQuantileAgent.num_tau_samples = 64 @@ -12,12 +11,12 @@ JaxImplicitQuantileAgent.num_tau_prime_samples = 64 JaxImplicitQuantileAgent.num_quantile_samples = 32 JaxImplicitQuantileAgent.gamma = 0.99 JaxImplicitQuantileAgent.update_horizon = 3 -JaxImplicitQuantileAgent.min_replay_history = 20000 # agent steps +JaxImplicitQuantileAgent.min_replay_history = 20_000 # agent steps JaxImplicitQuantileAgent.update_period = 4 -JaxImplicitQuantileAgent.target_update_period = 8000 # agent steps +JaxImplicitQuantileAgent.target_update_period = 8_000 # agent steps JaxImplicitQuantileAgent.epsilon_train = 0.01 JaxImplicitQuantileAgent.epsilon_eval = 0.001 -JaxImplicitQuantileAgent.epsilon_decay_period = 250000 # agent steps +JaxImplicitQuantileAgent.epsilon_decay_period = 250_000 # agent steps JaxImplicitQuantileAgent.optimizer = 'adam' create_optimizer.learning_rate = 0.00005 create_optimizer.eps = 0.0003125 @@ -29,9 +28,9 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_implicit_quantile' create_agent.debug_mode = True Runner.num_iterations = 200 -Runner.training_steps = 250000 -Runner.evaluation_steps = 125000 -Runner.max_steps_per_episode = 27000 +Runner.training_steps = 250_000 +Runner.evaluation_steps = 125_000 +Runner.max_steps_per_episode = 27_000 -OutOfGraphReplayBuffer.replay_capacity = 1000000 -OutOfGraphReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 diff --git a/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile_profiling.gin b/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile_profiling.gin index 1433c285..c281e81e 100644 --- a/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile_profiling.gin +++ b/dopamine/jax/agents/implicit_quantile/configs/implicit_quantile_profiling.gin @@ -5,7 +5,6 @@ import dopamine.jax.agents.implicit_quantile.implicit_quantile_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer JaxImplicitQuantileAgent.kappa = 1.0 JaxImplicitQuantileAgent.num_tau_samples = 64 @@ -15,7 +14,7 @@ JaxImplicitQuantileAgent.gamma = 0.99 JaxImplicitQuantileAgent.update_horizon = 3 JaxImplicitQuantileAgent.min_replay_history = 100 # agent steps JaxImplicitQuantileAgent.update_period = 4 -JaxImplicitQuantileAgent.target_update_period = 8000 # agent steps +JaxImplicitQuantileAgent.target_update_period = 8_000 # agent steps JaxImplicitQuantileAgent.epsilon_fn = @dqn_agent.identity_epsilon JaxImplicitQuantileAgent.epsilon_train = 0.0 JaxImplicitQuantileAgent.epsilon_eval = 0.0 @@ -30,9 +29,9 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_implicit_quantile' create_agent.debug_mode = True Runner.num_iterations = 200 -Runner.training_steps = 250000 -Runner.evaluation_steps = 125000 -Runner.max_steps_per_episode = 27000 +Runner.training_steps = 250_000 +Runner.evaluation_steps = 125_000 +Runner.max_steps_per_episode = 27_000 -OutOfGraphReplayBuffer.replay_capacity = 1000000 -OutOfGraphReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 diff --git a/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent.py b/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent.py index bc8a8e74..aa15ca16 100644 --- a/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent.py +++ b/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent.py @@ -18,10 +18,6 @@ Distributional RL" (Dabney et. al, 2018). """ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - import functools from dopamine.jax import networks diff --git a/dopamine/jax/agents/ppo/__init__.py b/dopamine/jax/agents/ppo/__init__.py new file mode 100644 index 00000000..a98a65d0 --- /dev/null +++ b/dopamine/jax/agents/ppo/__init__.py @@ -0,0 +1,14 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/dopamine/jax/agents/ppo/configs/ppo.gin b/dopamine/jax/agents/ppo/configs/ppo.gin new file mode 100644 index 00000000..1c66b29e --- /dev/null +++ b/dopamine/jax/agents/ppo/configs/ppo.gin @@ -0,0 +1,45 @@ +# Mujoco Hyperparameters following those specified in Table 3 of Appendix A in: +# "Proximal Policy Optimization Algorithms" +# by John Schulman et al. +# https://arxiv.org/abs/1707.06347 +import dopamine.continuous_domains.run_experiment +import dopamine.discrete_domains.gym_lib +import dopamine.jax.agents.ppo.ppo_agent +import dopamine.jax.agents.dqn.dqn_agent +import dopamine.jax.continuous_networks +import dopamine.jax.replay_memory.replay_buffer + +PPOAgent.network = @continuous_networks.PPOActorCriticNetwork +PPOAgent.num_layers = 2 +PPOAgent.hidden_units = 64 +PPOAgent.activation = 'tanh' +PPOAgent.update_period = 2048 +PPOAgent.optimizer = 'adam' +PPOAgent.max_gradient_norm = 0.5 +create_optimizer.learning_rate = 3e-4 +create_optimizer.eps = 1e-5 +create_optimizer.anneal_learning_rate = True +create_optimizer.anneal_steps = 160_000 # 500 iterations * 10 epochs * 2048 timesteps / 64 batches +PPOAgent.num_epochs = 10 +PPOAgent.batch_size = 64 +PPOAgent.gamma = 0.99 +PPOAgent.lambda_ = 0.95 +PPOAgent.epsilon = 0.2 +PPOAgent.vf_coefficient = 0.5 +PPOAgent.entropy_coefficient = 0.0 +PPOAgent.clip_critic_loss = True +PPOAgent.seed = None # Seed with the current time + +create_gym_environment.environment_name = 'HalfCheetah' +create_gym_environment.version = 'v2' +create_gym_environment.use_legacy_gym = True +create_gym_environment.use_ppo_preprocessing = True +create_continuous_runner.schedule = 'continuous_train' +create_continuous_agent.agent_name = 'ppo' +ContinuousTrainRunner.create_environment_fn = @gym_lib.create_gym_environment +ContinuousRunner.num_iterations = 500 +ContinuousRunner.training_steps = 2048 +ContinuousRunner.max_steps_per_episode = None + +ReplayBuffer.max_capacity = 2048 +ReplayBuffer.batch_size = 2048 diff --git a/dopamine/jax/agents/ppo/configs/ppo_atari.gin b/dopamine/jax/agents/ppo/configs/ppo_atari.gin new file mode 100644 index 00000000..b023725a --- /dev/null +++ b/dopamine/jax/agents/ppo/configs/ppo_atari.gin @@ -0,0 +1,44 @@ +# Atari Hyperparameters based on those specified in Table 5 of Appendix A in: +# "Proximal Policy Optimization Algorithms" +# by John Schulman et al. +# https://arxiv.org/abs/1707.06347 +# Note: This is a single actor implementation, thus we multiply the batch size +# and update period by 8 to be closer to the original implementation that used 8 +# actors. This gives use the same number of samples per training iteration using +# one actor. +import dopamine.discrete_domains.atari_lib +import dopamine.discrete_domains.run_experiment +import dopamine.jax.agents.ppo.ppo_agent +import dopamine.jax.agents.dqn.dqn_agent +import dopamine.jax.networks +import dopamine.jax.replay_memory.replay_buffer + +PPOAgent.network = @networks.PPODiscreteActorCriticNetwork +PPOAgent.update_period = 1024 # 8 * 128 +PPOAgent.optimizer = 'adam' +PPOAgent.max_gradient_norm = 0.5 +create_optimizer.learning_rate = 2.5e-4 +create_optimizer.eps = 1e-5 +create_optimizer.anneal_learning_rate = True +create_optimizer.anneal_steps = 117_600 # 980 iterations * 3 epochs * 10240 timesteps / 256 batches +PPOAgent.num_epochs = 3 +PPOAgent.batch_size = 256 # 8 * 32 +PPOAgent.gamma = 0.99 +PPOAgent.lambda_ = 0.95 +PPOAgent.epsilon = 0.1 +PPOAgent.vf_coefficient = 0.5 +PPOAgent.entropy_coefficient = 0.01 +PPOAgent.clip_critic_loss = True +PPOAgent.seed = None # Seed with the current time + +atari_lib.create_atari_environment.game_name = 'Pong' +atari_lib.create_atari_environment.use_ppo_preprocessing = True +create_runner.schedule = 'continuous_train' +create_agent.agent_name = 'ppo' +create_agent.debug_mode = True +Runner.num_iterations = 980 +Runner.training_steps = 10240 +Runner.max_steps_per_episode = None + +ReplayBuffer.max_capacity = 1024 +ReplayBuffer.batch_size = 1024 diff --git a/dopamine/jax/agents/ppo/ppo_agent.py b/dopamine/jax/agents/ppo/ppo_agent.py new file mode 100644 index 00000000..024e6693 --- /dev/null +++ b/dopamine/jax/agents/ppo/ppo_agent.py @@ -0,0 +1,773 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Compact implementation of a PPO agent in JAX. + +Based on agent described in + "Proximal Policy Optimization Algorithms" + by John Schulman et al. + https://arxiv.org/abs/1707.06347 +""" + +import functools +import time + +from absl import logging +from dopamine.jax import continuous_networks +from dopamine.jax import losses +from dopamine.jax.agents.dqn import dqn_agent +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers +from dopamine.metrics import statistics_instance +import flax +from flax import linen as nn +from flax.metrics import tensorboard +import gin +import jax +import jax.numpy as jnp +import numpy as np +import optax +import tensorflow as tf + + +def train( + network_def: nn.Module, + network_params: flax.core.FrozenDict, + optim: optax.GradientTransformation, + optimizer_state: jnp.ndarray, + key: jnp.ndarray, + states: jnp.ndarray, + actions: jnp.ndarray, + next_states: jnp.ndarray, + rewards: jnp.ndarray, + terminals: jnp.ndarray, + num_epochs: int, + batch_size: int, + epsilon: float, + gamma: float, + lambda_: float, + vf_coefficient: float, + entropy_coefficient: float, + clip_critic_loss: bool, +): + """Run the training step. + + Args: + network_def: The PPO network definition. + network_params: Parameters for PPO's online network. + optim: The PPO optimizer. + optimizer_state: The PPO optimizer state. + key: An rng key to use for random action selection. + states: A trajectory of states. + actions: A trajectory of actions. + next_states: A trajectory of next states. + rewards: A trajectory of rewards. + terminals: A trajectory of terminals. + num_epochs: The number of epochs to run. + batch_size: The batch size to use. + epsilon: The epsilon clipping for PPO. + gamma: The discount factor. + lambda_: The GAE parameter for calculating advantage function. + vf_coefficient: The value function coefficient for PPO. + entropy_coefficient: The entropy coefficient for PPO. + clip_critic_loss: Whether to clip the critic loss. + + Returns: + network_params: The updated network parameters. + optimizer_state: The updated optimizer state. + loss_stats: Training statistics. + """ + q_values = jax.vmap( + lambda state: network_def.apply( + network_params, state, method=network_def.critic + ).q_value + )(states) + q_values = jax.lax.stop_gradient(q_values) + q_values = jnp.squeeze(q_values) + + # Next q value for the last collected state used in the delta (12) calcuation + # to estimate future rewards beyond the current trajectory. + # Need to apply whole network as next action has not be sampled yet. + rng, key = jax.random.split(key) + next_q_value = network_def.apply( + network_params, next_states[-1], rng + ).critic.q_value + next_q_value = jax.lax.stop_gradient(next_q_value) + next_q_value = jnp.squeeze(next_q_value) + + advantages, returns = calculate_advantages_and_returns( + q_values, + next_q_value, + rewards, + terminals, + gamma, + lambda_, + ) + + log_probability = jax.vmap( + lambda state, action: network_def.apply( + network_params, state, action=action, method=network_def.actor + ).log_probability + )(states, actions) + log_probability = jax.lax.stop_gradient(log_probability) + + batch_keys = jnp.stack(jax.random.split(key, num=states.shape[0])) + sampled_actions = jax.vmap( + lambda state, key: network_def.apply( + network_params, state, key=key, method=network_def.actor + ).sampled_action + )(states, batch_keys) + sampled_actions = jnp.mean(sampled_actions, axis=0) + + ( + num_batches, + states, + actions, + returns, + advantages, + log_probability, + q_values, + ) = create_minibatches_and_shuffle( + states, + actions, + returns, + advantages, + log_probability, + q_values, + batch_size, + key, + ) + + loss_stats = { + 'combined_loss': [], + 'actor_loss': [], + 'critic_loss': [], + 'entropy_loss': [], + } + for _ in range(num_epochs): + for i in range(num_batches): + network_params, optimizer_state, aux_vars = train_minibatch( + network_def, + network_params, + optim, + optimizer_state, + states[i], + actions[i], + returns[i], + advantages[i], + log_probability[i], + q_values[i], + epsilon, + vf_coefficient, + entropy_coefficient, + clip_critic_loss, + ) + for k, v in aux_vars.items(): + if k in loss_stats: + loss_stats[k].append(v.item()) + + loss_stats = { + 'Losses/Combined': np.mean(loss_stats.get('combined_loss', 0.0)), + 'Losses/Actor': np.mean(loss_stats.get('actor_loss', 0.0)), + 'Losses/Critic': np.mean(loss_stats.get('critic_loss', 0.0)), + 'Losses/Entropy': np.mean(loss_stats.get('entropy_loss', 0.0)), + } + for i, a in enumerate(sampled_actions): + loss_stats.update({f'Values/SampledAction{i}': a}) + return network_params, optimizer_state, loss_stats + + +def calculate_advantages_and_returns( + q_values: jnp.ndarray, + next_q_value: jnp.ndarray, + rewards: jnp.ndarray, + terminals: jnp.ndarray, + gamma: float, + lambda_: float, +): + """Calculates the advantages and returns.""" + nsteps = rewards.shape[0] + + # Generalized Advantage Estimation calculated based on equations (11) and (12) + advantages = np.zeros_like(rewards) + for t in reversed(range(nsteps)): + delta = ( + rewards[t] + + gamma + * (q_values[t + 1] if t < nsteps - 1 else next_q_value) + * (1.0 - terminals[t]) + - q_values[t] + ) + advantages[t] = delta + lambda_ * gamma * ( + advantages[t + 1] if t < nsteps - 1 else 0.0 + ) * (1.0 - terminals[t]) + # returns: Total discounted future rewards (R + yV(s')) + # -> Calculated by adding V(s) to advantages (R + yV(s') - V(s)) + returns = advantages + q_values + return jnp.array(advantages), jnp.array(returns) + + +def create_minibatches_and_shuffle( + states: jnp.ndarray, + actions: jnp.ndarray, + returns: jnp.ndarray, + advantages: jnp.ndarray, + log_probability: jnp.ndarray, + q_values: jnp.ndarray, + batch_size: int, + key: jnp.ndarray, +): + """Create and shuffle minibatches for training.""" + assert ( + states.shape[0] % batch_size == 0 + ), 'Batch size must divide number of states.' + num_batches = states.shape[0] // batch_size + + states = states.reshape((num_batches, batch_size) + states.shape[1:]) + actions = actions.reshape((num_batches, batch_size) + actions.shape[1:]) + returns = returns.reshape((num_batches, batch_size) + returns.shape[1:]) + advantages = advantages.reshape( + (num_batches, batch_size) + advantages.shape[1:] + ) + log_probability = log_probability.reshape( + (num_batches, batch_size) + log_probability.shape[1:] + ) + q_values = q_values.reshape((num_batches, batch_size) + q_values.shape[1:]) + + indices = jax.random.permutation(key, num_batches) + states = states[indices] + actions = actions[indices] + returns = returns[indices] + advantages = advantages[indices] + log_probability = log_probability[indices] + q_values = q_values[indices] + + return ( + num_batches, + states, + actions, + returns, + advantages, + log_probability, + q_values, + ) + + +@functools.partial( + jax.jit, + static_argnames=[ + 'network_def', + 'optim', + 'epsilon', + 'vf_coefficient', + 'entropy_coefficient', + 'clip_critic_loss', + ], +) +def train_minibatch( + network_def: nn.Module, + network_params: flax.core.FrozenDict, + optim: optax.GradientTransformation, + optimizer_state: jnp.ndarray, + states: jnp.ndarray, + actions: jnp.ndarray, + returns: jnp.ndarray, + advantages: jnp.ndarray, + log_probability: jnp.ndarray, + q_values: jnp.ndarray, + epsilon: float, + vf_coefficient: float, + entropy_coefficient: float, + clip_critic_loss: bool, +): + """Run the training step for a minibatch.""" + + def loss_fn( + network_params: flax.core.FrozenDict, + states: jnp.ndarray, + actions: jnp.ndarray, + returns: jnp.ndarray, + advantages: jnp.ndarray, + old_log_probability: jnp.ndarray, + old_q_values: jnp.ndarray, + ): + """Calculates the loss of a minibatch.""" + actor_output = jax.vmap( + lambda state, action: network_def.apply( + network_params, + state, + action=action, + method=network_def.actor, + ) + )(states, actions) + log_probability = actor_output.log_probability + ratio = jnp.exp(log_probability - old_log_probability) + actor_loss = jnp.mean( + -jnp.minimum( + ratio * advantages, + jnp.clip(ratio, 1.0 - epsilon, 1.0 + epsilon) * advantages, + ) + ) + + q_values = jax.vmap( + lambda state: network_def.apply( + network_params, state, method=network_def.critic + ).q_value + )(states) + q_values = jnp.squeeze(q_values) + # Scale critic loss by 0.5 to match baselines implementation. + if clip_critic_loss: + # Value function loss clipping as specified in: + # https://ppo-details.cleanrl.dev/2021/11/05/ppo-implementation-details/ + # Note: May hurt performance according to above + critic_loss = 0.5 * jnp.mean( + jnp.maximum( + losses.mse_loss(q_values, returns), + losses.mse_loss( + jnp.clip( + q_values, old_q_values - epsilon, old_q_values + epsilon + ), + returns, + ), + ) + ) + else: + critic_loss = 0.5 * jnp.mean(losses.mse_loss(q_values, returns)) + + entropy = actor_output.entropy if actor_output.entropy is not None else 0.0 + entropy_loss = jnp.mean(entropy) + + loss = ( + actor_loss + + vf_coefficient * critic_loss + - entropy_coefficient * entropy_loss + ) + + return loss, { + 'combined_loss': loss, + 'actor_loss': actor_loss, + 'critic_loss': critic_loss, + 'entropy_loss': entropy_loss, + } + + grad_fn = jax.value_and_grad(loss_fn, argnums=(0), has_aux=True) + + # Normalize advantages within minibatch. + advantages = jnp.array( + (advantages - advantages.mean()) / (advantages.std() + 1e-8) + ) + + (_, aux_vars), grad = grad_fn( + network_params, + states, + actions, + returns, + advantages, + log_probability, + q_values, + ) + + updates, optimizer_state = optim.update( + grad, optimizer_state, params=network_params + ) + network_params = optax.apply_updates(network_params, updates) + + return network_params, optimizer_state, aux_vars + + +@functools.partial(jax.jit, static_argnames=['network_def']) +def select_action(network_def, params, state, rng): + """Sample an action to take from the current policy network. + + This obtains a mean and variance from the input policy network, and samples an + action using a Gaussian distribution. + + Args: + network_def: Linen Module to use for inference. + params: Linen params (frozen dict) to use for inference. + state: input state to use for inference. + rng: Jax random number generator. + + Returns: + rng: Jax random number generator. + action: The selected action. + """ + rng, rng2 = jax.random.split(rng) + action = network_def.apply( + params, state, rng2, method=network_def.actor + ).sampled_action + action = jax.lax.stop_gradient(action) + return rng, action + + +@gin.configurable +class PPOAgent(dqn_agent.JaxDQNAgent): + """A JAX implementation of the PPO agent.""" + + def __init__( + self, + action_shape, + observation_shape, + action_limits, + stack_size=1, + update_horizon=1, + network=continuous_networks.PPOActorCriticNetwork, + num_layers=2, + hidden_units=64, + activation='tanh', + update_period=2048, + num_epochs=10, + batch_size=64, + gamma=0.99, + lambda_=0.95, + epsilon=0.2, + vf_coefficient=0.5, + entropy_coefficient=0.0, + clip_critic_loss=True, + eval_mode=False, + optimizer='adam', + max_gradient_norm=0.5, + summary_writer=None, + summary_writing_frequency=1, + allow_partial_reload=False, + seed=None, + collector_allowlist='tensorboard', + ): + r"""Initializes the agent and constructs the necessary components. + + Args: + action_shape: int or tuple, dimensionality of the action space. + observation_shape: tuple of ints describing the observation shape. + action_limits: pair of lower and higher bounds for actions. + stack_size: int, number of frames to use in state stack. + update_horizon: int, replay buffer update horizon. + network: PPO network to use for training. + num_layers: int, number of layers in the network. + hidden_units: int, number of hidden units in the network. + activation: nn.Module, activation function to use in the network. + update_period: int, period between PPO updates. + num_epochs: int, number of epochs to run the agent for. + batch_size: int, number of elements to sample from the replay buffer. + gamma: float, discount factor with the usual RL meaning. + lambda_: float, GAE parameter for calculating advantage function. + epsilon: float, epsilon clipping for PPO. + vf_coefficient: float, value function coefficient for PPO. + entropy_coefficient: float, entropy coefficient for PPO. + clip_critic_loss: bool, whether to clip critic loss. + eval_mode: bool, True for evaluation and False for training. + optimizer: str, name of optimizer to use. + max_gradient_norm: float, maximum gradient norm for optimizer. + summary_writer: SummaryWriter object for outputting training statistics. + summary_writing_frequency: int, frequency with which summaries will be + written. Lower values will result in slower training. + allow_partial_reload: bool, whether we allow reloading a partial agent + (for instance, only the network parameters). + seed: int, a seed for PPO's internal RNG, used for initialization and + sampling actions. + collector_allowlist: list of str, if using CollectorDispatcher, this can + be used to specify which Collectors to log to. + """ + assert isinstance(observation_shape, tuple) + self._seed = int(time.time() * 1e6) if seed is None else seed + + self._use_new_replay_buffer = True + self._replay_scheme = 'uniform' + + if isinstance(action_shape, int): + action_shape = (action_shape,) + + logging.info( + 'Creating %s agent with the following parameters:', + self.__class__.__name__, + ) + logging.info('\t action_shape: %s', action_shape) + logging.info('\t action_limits: %s', action_limits) + logging.info('\t observation_shape: %s', observation_shape) + logging.info('\t num_layers: %d', num_layers) + logging.info('\t hidden_units: %d', hidden_units) + logging.info('\t update_period: %d', update_period) + logging.info('\t num_epochs: %d', num_epochs) + logging.info('\t batch_size: %d', batch_size) + logging.info('\t gamma: %f', gamma) + logging.info('\t lambda_: %f', lambda_) + logging.info('\t epsilon: %f', epsilon) + logging.info('\t vf_coefficient: %f', vf_coefficient) + logging.info('\t entropy_coefficient: %f', entropy_coefficient) + logging.info('\t clip_critic_loss: %s', clip_critic_loss) + logging.info('\t optimizer: %s', optimizer) + logging.info('\t seed: %d', self._seed) + + self.action_shape = action_shape + if action_limits is not None: + action_limits = tuple(tuple(x.reshape(-1)) for x in action_limits) + self.observation_shape = tuple(observation_shape) + if network.__name__ == 'PPOActorCriticNetwork': + self.network_def = network( + action_shape=action_shape, + action_limits=action_limits, + num_layers=num_layers, + hidden_units=hidden_units, + activation=continuous_networks.create_activation(activation), + ) + else: + self.network_def = network( + action_shape=action_shape, + action_limits=action_limits, + ) + self.stack_size = stack_size + self.update_horizon = update_horizon + self.update_period = update_period + self.num_epochs = num_epochs + self.batch_size = batch_size + self.gamma = gamma + self.lambda_ = lambda_ + self.epsilon = epsilon + self.vf_coefficient = vf_coefficient + self.entropy_coefficient = entropy_coefficient + self.clip_critic_loss = clip_critic_loss + self.eval_mode = eval_mode + + self.training_steps = 0 + if isinstance(summary_writer, str): + try: + tf.compat.v1.enable_v2_behavior() + except ValueError: + pass + self.summary_writer = tf.summary.create_file_writer(summary_writer) + else: + self.summary_writer = summary_writer + self.summary_writing_frequency = summary_writing_frequency + self.allow_partial_reload = allow_partial_reload + self._collector_allowlist = collector_allowlist + + self._rng = jax.random.PRNGKey(self._seed) + state_shape = self.observation_shape + (self.stack_size,) + self.state = np.zeros(state_shape) + self._replay = self._build_replay_buffer() + self._optimizer_name = optimizer + self._max_gradient_norm = max_gradient_norm + self._build_networks_and_optimizer() + + # Variables to be initialized by the agent once it interacts with the + # environment. + self._observation = None + self._last_observation = None + + def _build_networks_and_optimizer(self): + self._rng, init_key = jax.random.split(self._rng) + + # We can reuse init_key safely for the action selection key + # since it is only used for shape inference during initialization. + self.network_params = self.network_def.init(init_key, self.state, init_key) + self.network_optimizer = optax.chain( + # Perform global gradient clipping as specified in: + # https://ppo-details.cleanrl.dev/2021/11/05/ppo-implementation-details + optax.clip_by_global_norm(self._max_gradient_norm), + dqn_agent.create_optimizer(self._optimizer_name), + ) + self.optimizer_state = self.network_optimizer.init(self.network_params) + + def _build_replay_buffer(self): + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=self.stack_size, + update_horizon=self.update_horizon, + gamma=self.gamma, + ) + sampling_distribution = samplers.SequentialSamplingDistribution( + seed=self._seed, + sort_samples=False, + ) + return replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=sampling_distribution, + ) + + def begin_episode(self, observation): + """Returns the agent's first action for this episode. + + Args: + observation: numpy array, the environment's initial observation. + + Returns: + int, the selected action. + """ + self._reset_state() + self._record_observation(observation) + + if not self.eval_mode: + self._train_step() + + self._rng, self.action = select_action( + self.network_def, + self.network_params, + self.state, + self._rng, + ) + self.action = np.asarray(self.action) + return self.action + + def step(self, reward, observation): + """Records the most recent transition and returns the agent's next action. + + We store the observation of the last time step since we want to store it + with the reward. + + Args: + reward: float, the reward received from the agent's most recent action. + observation: numpy array, the most recent observation. + + Returns: + int, the selected action. + """ + self._last_observation = self._observation + self._record_observation(observation) + + if not self.eval_mode: + self._store_transition(self._last_observation, self.action, reward, False) + self._train_step() + + self._rng, self.action = select_action( + self.network_def, + self.network_params, + self.state, + self._rng, + ) + self.action = np.asarray(self.action) + return self.action + + def _train_step(self): + """Runs a single training step.""" + if self._replay.add_count >= self.update_period: + self._sample_from_replay_buffer() + self._rng, key = jax.random.split(self._rng) + + self.network_params, self.optimizer_state, loss_stats = train( + self.network_def, + self.network_params, + self.network_optimizer, + self.optimizer_state, + key, + self.replay_elements['state'], + self.replay_elements['action'], + self.replay_elements['next_state'], + self.replay_elements['reward'], + self.replay_elements['terminal'], + self.num_epochs, + self.batch_size, + self.epsilon, + self.gamma, + self.lambda_, + self.vf_coefficient, + self.entropy_coefficient, + self.clip_critic_loss, + ) + self._replay.clear() + # re-add last next state for continuity + self._record_observation(self._last_observation) + + if ( + self.summary_writer is not None + and self.training_steps > 0 + and self.training_steps % self.summary_writing_frequency == 0 + ): + statistics = [] + for k in loss_stats: + if isinstance(self.summary_writer, tensorboard.SummaryWriter): + self.summary_writer.scalar(k, loss_stats[k], self.training_steps) + else: + with self.summary_writer.as_default(): + tf.summary.scalar(k, loss_stats[k], self.training_steps) + if hasattr(self, 'collector_dispatcher'): + statistics.append( + statistics_instance.StatisticsInstance( + k, + np.asarray(loss_stats[k]), + step=self.training_steps, + ) + ) + if hasattr(self, 'collector_dispatcher'): + self.collector_dispatcher.write( + statistics, collector_allowlist=self._collector_allowlist + ) + self.summary_writer.flush() + self.training_steps += 1 + + def bundle_and_checkpoint(self, checkpoint_dir, iteration_number): + """Returns a self-contained bundle of the agent's state. + + This is used for checkpointing. It will return a dictionary containing all + non-TensorFlow objects (to be saved into a file by the caller), and it saves + all TensorFlow objects into a checkpoint file. + + Args: + checkpoint_dir: str, directory where TensorFlow objects will be saved. + iteration_number: int, iteration number to use for naming the checkpoint + file. + + Returns: + A dict containing additional Python objects to be checkpointed by the + experiment. If the checkpoint directory does not exist, returns None. + """ + if not tf.io.gfile.exists(checkpoint_dir): + return None + # Checkpoint the out-of-graph replay buffer. + self._replay.save(checkpoint_dir, iteration_number) + bundle_dictionary = { + 'state': self.state, + 'training_steps': self.training_steps, + 'network_params': self.network_params, + 'optimizer_state': self.optimizer_state, + } + return bundle_dictionary + + def unbundle(self, checkpoint_dir, iteration_number, bundle_dictionary): + """Restores the agent from a checkpoint. + + Restores the agent's Python objects to those specified in bundle_dictionary, + and restores the TensorFlow objects to those specified in the + checkpoint_dir. If the checkpoint_dir does not exist, will not reset the + agent's state. + + Args: + checkpoint_dir: str, path to the checkpoint saved. + iteration_number: int, checkpoint version, used when restoring the replay + buffer. + bundle_dictionary: dict, containing additional Python objects owned by the + agent. + + Returns: + bool, True if unbundling was successful. + """ + try: + # self._replay.load() will throw a NotFoundError if it does not find all + # the necessary files. + self._replay.load(checkpoint_dir, iteration_number) + except tf.errors.NotFoundError: + if not self.allow_partial_reload: + # If we don't allow partial reloads, we will return False. + return False + logging.warning('Unable to reload replay buffer!') + if bundle_dictionary is not None: + self.state = bundle_dictionary['state'] + self.training_steps = bundle_dictionary['training_steps'] + + self.network_params = bundle_dictionary['network_params'] + self.network_optimizer = dqn_agent.create_optimizer(self._optimizer_name) + self.optimizer_state = bundle_dictionary['optimizer_state'] + elif not self.allow_partial_reload: + return False + else: + logging.warning("Unable to reload the agent's parameters!") + return True diff --git a/dopamine/jax/agents/quantile/configs/quantile.gin b/dopamine/jax/agents/quantile/configs/quantile.gin index 9f6eb02d..e2808931 100644 --- a/dopamine/jax/agents/quantile/configs/quantile.gin +++ b/dopamine/jax/agents/quantile/configs/quantile.gin @@ -4,7 +4,6 @@ import dopamine.jax.agents.quantile.quantile_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxQuantileAgent.kappa = 1.0 JaxQuantileAgent.num_atoms = 200 @@ -32,5 +31,6 @@ Runner.training_steps = 250000 Runner.evaluation_steps = 125000 Runner.max_steps_per_episode = 27000 -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/quantile/configs/quantile_profiling.gin b/dopamine/jax/agents/quantile/configs/quantile_profiling.gin index 3c44170a..85732155 100644 --- a/dopamine/jax/agents/quantile/configs/quantile_profiling.gin +++ b/dopamine/jax/agents/quantile/configs/quantile_profiling.gin @@ -2,7 +2,6 @@ import dopamine.jax.agents.quantile.quantile_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent -import dopamine.replay_memory.prioritized_replay_buffer JaxQuantileAgent.kappa = 1.0 JaxQuantileAgent.num_atoms = 200 @@ -30,5 +29,6 @@ Runner.training_steps = 250000 Runner.evaluation_steps = 125000 Runner.max_steps_per_episode = 27000 -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/quantile/quantile_agent.py b/dopamine/jax/agents/quantile/quantile_agent.py index ff7d979a..31947ac6 100644 --- a/dopamine/jax/agents/quantile/quantile_agent.py +++ b/dopamine/jax/agents/quantile/quantile_agent.py @@ -18,16 +18,14 @@ Regression" - Dabney et. al, 2017" """ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - import functools from dopamine.jax import networks from dopamine.jax.agents.dqn import dqn_agent +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers from dopamine.metrics import statistics_instance -from dopamine.replay_memory import prioritized_replay_buffer import gin import jax import jax.numpy as jnp @@ -235,14 +233,18 @@ def _build_replay_buffer(self): """Creates the replay buffer used by the agent.""" if self._replay_scheme not in ['uniform', 'prioritized']: raise ValueError('Invalid replay scheme: {}'.format(self._replay_scheme)) - # Both replay schemes use the same data structure, but the 'uniform' scheme - # sets all priorities to the same value (which yields uniform sampling). - return prioritized_replay_buffer.OutOfGraphPrioritizedReplayBuffer( - observation_shape=self.observation_shape, + + transition_accumulator = accumulator.TransitionAccumulator( stack_size=self.stack_size, update_horizon=self.update_horizon, gamma=self.gamma, - observation_dtype=self.observation_dtype, + ) + sampling_distribution = samplers.PrioritizedSamplingDistribution( + seed=self._seed + ) + return replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=sampling_distribution, ) def _train_step(self): @@ -289,8 +291,9 @@ def _train_step(self): # small nonzero value to the loss to avoid 0 priority items. While # technically this may be okay, setting all items to 0 priority will # cause troubles, and also result in 1.0 / 0.0 = NaN correction terms. - self._replay.set_priority( - self.replay_elements['indices'], jnp.sqrt(loss + 1e-10) + self._replay.update( + self.replay_elements['indices'], + priorities=jnp.sqrt(loss + 1e-10), ) # Weight the loss by the inverse priorities. diff --git a/dopamine/jax/agents/rainbow/configs/c51.gin b/dopamine/jax/agents/rainbow/configs/c51.gin index a132a1ec..8c4d849a 100644 --- a/dopamine/jax/agents/rainbow/configs/c51.gin +++ b/dopamine/jax/agents/rainbow/configs/c51.gin @@ -5,7 +5,6 @@ import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxRainbowAgent.num_atoms = 51 JaxRainbowAgent.vmax = 10. @@ -33,5 +32,6 @@ Runner.training_steps = 250000 # agent steps Runner.evaluation_steps = 125000 # agent steps Runner.max_steps_per_episode = 27000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/rainbow/configs/c51_acrobot.gin b/dopamine/jax/agents/rainbow/configs/c51_acrobot.gin index 122df428..dc366000 100644 --- a/dopamine/jax/agents/rainbow/configs/c51_acrobot.gin +++ b/dopamine/jax/agents/rainbow/configs/c51_acrobot.gin @@ -4,7 +4,6 @@ import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE JaxRainbowAgent.observation_dtype = %jax_networks.ACROBOT_OBSERVATION_DTYPE @@ -31,9 +30,10 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_rainbow' TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 500 -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/c51_cartpole.gin b/dopamine/jax/agents/rainbow/configs/c51_cartpole.gin index 9d311ffc..fd55a0d9 100644 --- a/dopamine/jax/agents/rainbow/configs/c51_cartpole.gin +++ b/dopamine/jax/agents/rainbow/configs/c51_cartpole.gin @@ -4,7 +4,6 @@ import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE JaxRainbowAgent.observation_dtype = %jax_networks.CARTPOLE_OBSERVATION_DTYPE @@ -34,9 +33,10 @@ create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 400 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 200 # Default max episode length. -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/c51_lunarlander.gin b/dopamine/jax/agents/rainbow/configs/c51_lunarlander.gin index 6701036c..e662d40c 100644 --- a/dopamine/jax/agents/rainbow/configs/c51_lunarlander.gin +++ b/dopamine/jax/agents/rainbow/configs/c51_lunarlander.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxRainbowAgent.observation_shape = %gym_lib.LUNAR_OBSERVATION_SHAPE @@ -32,11 +31,10 @@ create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 125 -Runner.training_steps = 4000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 1000 +Runner.training_steps = 4_000 +Runner.evaluation_steps = 1_000 +Runner.max_steps_per_episode = 1_000 -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 128 -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/c51_mountaincar.gin b/dopamine/jax/agents/rainbow/configs/c51_mountaincar.gin index 6bb07987..6d24ab3b 100644 --- a/dopamine/jax/agents/rainbow/configs/c51_mountaincar.gin +++ b/dopamine/jax/agents/rainbow/configs/c51_mountaincar.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxRainbowAgent.observation_shape = %gym_lib.MOUNTAINCAR_OBSERVATION_SHAPE @@ -35,10 +34,9 @@ create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 +Runner.training_steps = 1_000 Runner.max_steps_per_episode = 600 # Default max episode length. -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 128 -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/c51_profiling.gin b/dopamine/jax/agents/rainbow/configs/c51_profiling.gin index 5ced1eed..f40e4474 100644 --- a/dopamine/jax/agents/rainbow/configs/c51_profiling.gin +++ b/dopamine/jax/agents/rainbow/configs/c51_profiling.gin @@ -5,7 +5,6 @@ import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent -import dopamine.replay_memory.prioritized_replay_buffer JaxRainbowAgent.num_atoms = 51 JaxRainbowAgent.vmax = 10. @@ -13,7 +12,7 @@ JaxRainbowAgent.gamma = 0.99 JaxRainbowAgent.update_horizon = 1 JaxRainbowAgent.min_replay_history = 100 # agent steps JaxRainbowAgent.update_period = 4 -JaxRainbowAgent.target_update_period = 8000 # agent steps +JaxRainbowAgent.target_update_period = 8_000 # agent steps JaxRainbowAgent.epsilon_fn = @dqn_agent.identity_epsilon JaxRainbowAgent.epsilon_train = 0.0 JaxRainbowAgent.epsilon_eval = 0.0 @@ -26,9 +25,10 @@ create_runner.schedule = 'continuous_train' create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True Runner.num_iterations = 200 -Runner.training_steps = 250000 # agent steps -Runner.evaluation_steps = 125000 # agent steps -Runner.max_steps_per_episode = 27000 # agent steps +Runner.training_steps = 250_000 # agent steps +Runner.evaluation_steps = 125_000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/rainbow/configs/rainbow.gin b/dopamine/jax/agents/rainbow/configs/rainbow.gin index 43bf500e..7f7ff843 100644 --- a/dopamine/jax/agents/rainbow/configs/rainbow.gin +++ b/dopamine/jax/agents/rainbow/configs/rainbow.gin @@ -1,9 +1,10 @@ # Hyperparameters follow Hessel et al. (2018), except for sticky_actions, # which was False (not using sticky actions) in the original paper. import dopamine.jax.agents.rainbow.rainbow_agent +import dopamine.jax.replay_memory.replay_buffer +import dopamine.jax.replay_memory.samplers import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxRainbowAgent.num_atoms = 51 JaxRainbowAgent.vmax = 10. @@ -32,5 +33,6 @@ Runner.training_steps = 250000 # agent steps Runner.evaluation_steps = 125000 # agent steps Runner.max_steps_per_episode = 27000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 32 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/rainbow/configs/rainbow_acrobot.gin b/dopamine/jax/agents/rainbow/configs/rainbow_acrobot.gin index 25775fdd..e5a00bad 100644 --- a/dopamine/jax/agents/rainbow/configs/rainbow_acrobot.gin +++ b/dopamine/jax/agents/rainbow/configs/rainbow_acrobot.gin @@ -4,7 +4,6 @@ import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE JaxRainbowAgent.observation_dtype = %jax_networks.ACROBOT_OBSERVATION_DTYPE @@ -32,9 +31,10 @@ create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 500 -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/rainbow_cartpole.gin b/dopamine/jax/agents/rainbow/configs/rainbow_cartpole.gin index e700fdaa..c6a04416 100644 --- a/dopamine/jax/agents/rainbow/configs/rainbow_cartpole.gin +++ b/dopamine/jax/agents/rainbow/configs/rainbow_cartpole.gin @@ -4,7 +4,6 @@ import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE JaxRainbowAgent.observation_dtype = %jax_networks.CARTPOLE_OBSERVATION_DTYPE @@ -32,9 +31,10 @@ create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 -Runner.evaluation_steps = 1000 +Runner.training_steps = 1_000 +Runner.evaluation_steps = 1_000 Runner.max_steps_per_episode = 200 # Default max episode length. -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/rainbow_lunarlander.gin b/dopamine/jax/agents/rainbow/configs/rainbow_lunarlander.gin index 51975bb8..ac3b78a8 100644 --- a/dopamine/jax/agents/rainbow/configs/rainbow_lunarlander.gin +++ b/dopamine/jax/agents/rainbow/configs/rainbow_lunarlander.gin @@ -4,7 +4,6 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks -import dopamine.replay_memory.circular_replay_buffer import flax JaxRainbowAgent.observation_shape = %gym_lib.LUNAR_OBSERVATION_SHAPE @@ -30,11 +29,10 @@ create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 125 -Runner.training_steps = 4000 -Runner.evaluation_steps = 1000 -Runner.max_steps_per_episode = 1000 +Runner.training_steps = 4_000 +Runner.evaluation_steps = 1_000 +Runner.max_steps_per_episode = 1_000 -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 -OutOfGraphPrioritizedReplayBuffer.batch_size = 128 -OutOfGraphReplayBuffer.replay_capacity = 50000 -OutOfGraphReplayBuffer.batch_size = 128 +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/rainbow_mountaincar.gin b/dopamine/jax/agents/rainbow/configs/rainbow_mountaincar.gin index 59dbe091..667d89f6 100644 --- a/dopamine/jax/agents/rainbow/configs/rainbow_mountaincar.gin +++ b/dopamine/jax/agents/rainbow/configs/rainbow_mountaincar.gin @@ -33,10 +33,15 @@ create_agent.agent_name = 'jax_rainbow' create_agent.debug_mode = True TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 500 -Runner.training_steps = 1000 +Runner.training_steps = 1_000 Runner.max_steps_per_episode = 600 # Default max episode length. -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50000 +OutOfGraphPrioritizedReplayBuffer.replay_capacity = 50_000 OutOfGraphPrioritizedReplayBuffer.batch_size = 128 -OutOfGraphReplayBuffer.replay_capacity = 50000 +OutOfGraphReplayBuffer.replay_capacity = 50_000 OutOfGraphReplayBuffer.batch_size = 128 + +JaxRainbowAgent.use_new_replay_buffer = True # Set to False to use old buffer. +ReplayBuffer.max_capacity = 50_000 +ReplayBuffer.batch_size = 128 +PrioritizedSamplingDistribution.max_capacity = 50_000 diff --git a/dopamine/jax/agents/rainbow/configs/rainbow_profiling.gin b/dopamine/jax/agents/rainbow/configs/rainbow_profiling.gin index 2f6d60f2..854525db 100644 --- a/dopamine/jax/agents/rainbow/configs/rainbow_profiling.gin +++ b/dopamine/jax/agents/rainbow/configs/rainbow_profiling.gin @@ -35,3 +35,8 @@ Runner.max_steps_per_episode = 27000 # agent steps OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 OutOfGraphPrioritizedReplayBuffer.batch_size = 32 + +JaxRainbowAgent.use_new_replay_buffer = True # Set to False to use old buffer. +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/jax/agents/rainbow/rainbow_agent.py b/dopamine/jax/agents/rainbow/rainbow_agent.py index 94c02ba9..60567d89 100644 --- a/dopamine/jax/agents/rainbow/rainbow_agent.py +++ b/dopamine/jax/agents/rainbow/rainbow_agent.py @@ -33,17 +33,15 @@ Hessel et al. (2018). """ -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - import functools from dopamine.jax import losses from dopamine.jax import networks from dopamine.jax.agents.dqn import dqn_agent +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers from dopamine.metrics import statistics_instance -from dopamine.replay_memory import prioritized_replay_buffer import gin import jax import jax.numpy as jnp @@ -321,14 +319,18 @@ def _build_replay_buffer(self): """Creates the replay buffer used by the agent.""" if self._replay_scheme not in ['uniform', 'prioritized']: raise ValueError('Invalid replay scheme: {}'.format(self._replay_scheme)) - # Both replay schemes use the same data structure, but the 'uniform' scheme - # sets all priorities to the same value (which yields uniform sampling). - return prioritized_replay_buffer.OutOfGraphPrioritizedReplayBuffer( - observation_shape=self.observation_shape, + + transition_accumulator = accumulator.TransitionAccumulator( stack_size=self.stack_size, update_horizon=self.update_horizon, gamma=self.gamma, - observation_dtype=self.observation_dtype, + ) + sampling_distribution = samplers.PrioritizedSamplingDistribution( + seed=self._seed + ) + return replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=sampling_distribution, ) # TODO(psc): Refactor this so we have a class _select_action that calls @@ -450,14 +452,16 @@ def _train_step(self): if self._replay_scheme == 'prioritized': # Rainbow and prioritized replay are parametrized by an exponent - # alpha, but in both cases it is set to 0.5 - for simplicity's sake we - # leave it as is here, using the more direct sqrt(). Taking the square - # root "makes sense", as we are dealing with a squared loss. Add a - # small nonzero value to the loss to avoid 0 priority items. While - # technically this may be okay, setting all items to 0 priority will - # cause troubles, and also result in 1.0 / 0.0 = NaN correction terms. - self._replay.set_priority( - self.replay_elements['indices'], jnp.sqrt(loss + 1e-10) + # alpha, but in both cases it is set to 0.5 - for simplicity's sake + # we leave it as is here, using the more direct sqrt(). Taking the + # square root "makes sense", as we are dealing with a squared loss. + # Add a small nonzero value to the loss to avoid 0 priority items. + # While technically this may be okay, setting all items to 0 + # priority will cause troubles, and also result in 1.0 / 0.0 = NaN + # correction terms. + self._replay.update( + self.replay_elements['indices'], + priorities=jnp.sqrt(loss + 1e-10), ) if ( diff --git a/dopamine/jax/agents/sac/configs/sac.gin b/dopamine/jax/agents/sac/configs/sac.gin index 04e2bf49..52d1c0b0 100644 --- a/dopamine/jax/agents/sac/configs/sac.gin +++ b/dopamine/jax/agents/sac/configs/sac.gin @@ -7,15 +7,14 @@ import dopamine.discrete_domains.gym_lib import dopamine.jax.agents.sac.sac_agent import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.continuous_networks -import dopamine.replay_memory.circular_replay_buffer SACAgent.reward_scale_factor = 0.1 -SACAgent.network = @continuous_networks.SACNetwork +SACAgent.network = @continuous_networks.ActorCriticNetwork SACAgent.num_layers = 2 SACAgent.hidden_units = 256 SACAgent.gamma = 0.99 SACAgent.update_horizon = 1 -SACAgent.min_replay_history = 10000 # agent steps +SACAgent.min_replay_history = 10_000 # agent steps SACAgent.update_period = 1 SACAgent.target_update_type = 'soft' SACAgent.target_smoothing_coefficient = 0.005 @@ -33,12 +32,12 @@ create_gym_environment.version = 'v2' create_continuous_runner.schedule = 'continuous_train_and_eval' create_continuous_agent.agent_name = 'sac' ContinuousTrainRunner.create_environment_fn = @gym_lib.create_gym_environment -ContinuousRunner.num_iterations = 3200 -ContinuousRunner.training_steps = 1000 -ContinuousRunner.evaluation_steps = 10000 # agent steps -ContinuousRunner.max_steps_per_episode = 1000 +ContinuousRunner.num_iterations = 3_200 +ContinuousRunner.training_steps = 1_000 +ContinuousRunner.evaluation_steps = 10_000 # agent steps +ContinuousRunner.max_steps_per_episode = 1_000 ContinuousRunner.clip_rewards = False -circular_replay_buffer.OutOfGraphReplayBuffer.replay_capacity = 1000000 -circular_replay_buffer.OutOfGraphReplayBuffer.batch_size = 256 +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 256 diff --git a/dopamine/jax/agents/sac/sac_agent.py b/dopamine/jax/agents/sac/sac_agent.py index d158f2ec..85f6580c 100644 --- a/dopamine/jax/agents/sac/sac_agent.py +++ b/dopamine/jax/agents/sac/sac_agent.py @@ -30,6 +30,9 @@ from dopamine.jax import continuous_networks from dopamine.jax import losses from dopamine.jax.agents.dqn import dqn_agent +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers # pylint: disable=unused-import # This enables (experimental) networks for SAC from pixels. # Note, that the full name import is required to avoid a naming @@ -37,7 +40,6 @@ import dopamine.labs.sac_from_pixels.continuous_networks # pylint: enable=unused-import from dopamine.metrics import statistics_instance -from dopamine.replay_memory import circular_replay_buffer import flax from flax import linen as nn import gin @@ -222,8 +224,8 @@ def loss_fn( # This calculates the mean gradient/aux_vars using the individual # gradients/aux_vars from each item in the batch. - gradients = jax.tree_map(functools.partial(jnp.mean, axis=0), gradients) - aux_vars = jax.tree_map(functools.partial(jnp.mean, axis=0), aux_vars) + gradients = jax.tree.map(functools.partial(jnp.mean, axis=0), gradients) + aux_vars = jax.tree.map(functools.partial(jnp.mean, axis=0), aux_vars) network_gradient, alpha_gradient = gradients # Apply gradients to all the optimizers. @@ -249,7 +251,7 @@ def loss_fn( 'Values/CriticValues2': jnp.mean(aux_vars['critic_value_2']), 'Values/TargetValues1': jnp.mean(aux_vars['target_value_1']), 'Values/TargetValues2': jnp.mean(aux_vars['target_value_2']), - 'Values/Alpha': jnp.exp(log_alpha), + 'Values/Alpha': jnp.squeeze(jnp.exp(log_alpha)), } for i, a in enumerate(aux_vars['mean_action']): returns.update({f'Values/MeanActions{i}': a}) @@ -294,7 +296,7 @@ def __init__( observation_dtype=jnp.float32, reward_scale_factor=1.0, stack_size=1, - network=continuous_networks.SACNetwork, + network=continuous_networks.ActorCriticNetwork, num_layers=2, hidden_units=256, gamma=0.99, @@ -367,7 +369,7 @@ def __init__( if target_entropy is None: action_dim = functools.reduce(operator.mul, action_shape, 1.0) target_entropy = -0.5 * action_dim - seed = int(time.time() * 1e6) if seed is None else seed + self._seed = int(time.time() * 1e6) if seed is None else seed logging.info( 'Creating %s agent with the following parameters:', self.__class__.__name__, @@ -391,7 +393,7 @@ def __init__( ) logging.info('\t target_entropy: %f', target_entropy) logging.info('\t optimizer: %s', optimizer) - logging.info('\t seed: %d', seed) + logging.info('\t seed: %d', self._seed) self.action_shape = action_shape self.action_dtype = action_dtype @@ -420,9 +422,10 @@ def __init__( self.allow_partial_reload = allow_partial_reload self._collector_allowlist = collector_allowlist - self._rng = jax.random.PRNGKey(seed) + self._rng = jax.random.PRNGKey(self._seed) state_shape = self.observation_shape + (stack_size,) self.state = onp.zeros(state_shape) + self._replay_scheme = 'uniform' self._replay = self._build_replay_buffer() self._optimizer_name = optimizer self._build_networks_and_optimizer() @@ -451,14 +454,17 @@ def _build_networks_and_optimizer(self): def _build_replay_buffer(self): """Creates the replay buffer used by the agent.""" - return circular_replay_buffer.OutOfGraphReplayBuffer( - observation_shape=self.observation_shape, + transition_accumulator = accumulator.TransitionAccumulator( stack_size=self.stack_size, update_horizon=self.update_horizon, gamma=self.gamma, - observation_dtype=self.observation_dtype, - action_shape=self.action_shape, - action_dtype=self.action_dtype, + ) + sampling_distribution = samplers.UniformSamplingDistribution( + seed=self._seed + ) + return replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=sampling_distribution, ) def _maybe_sync_weights(self): @@ -475,7 +481,7 @@ def _sync_weights(target_p, online_p): + (1 - self.target_smoothing_coefficient) * target_p ) - self.target_params = jax.tree_map( + self.target_params = jax.tree.map( _sync_weights, self.target_params, self.network_params ) diff --git a/dopamine/jax/checkpointers.py b/dopamine/jax/checkpointers.py new file mode 100644 index 00000000..23c4bb01 --- /dev/null +++ b/dopamine/jax/checkpointers.py @@ -0,0 +1,142 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Checkpointer using Orbax and MessagePack. + +Usage: + 1) Implement `to_state_dict` and `from_state_dict` on your class. + 2) Create an `orbax.CheckpointManager` and use the `CheckpointHandler`. + + To save: call `save` on your checkpoint manager passing the class instance. + To restore: call `restore` on your checkpoint manager passing the class + instance. + +Example: + + ```py + from typing import Dict, Any + + import numpy.typing as npt + from orbax import checkpoint as orbax + from dopamine.jax import checkpoint + + class MyClass: + def __init__(self, data: npt.NDArray): + self._data = data + + def to_state_dict(self) -> Dict[str, Any]: + return {'data': self._data} + + def from_state_dict(self, state_dict: Dict[str, Any]) -> None: + self._data = state_dict['data'] + + checkpoint_manager = orbax.CheckpointManager( + my_workdir, + checkpointers=checkpoint.Checkpointer(), + ) + + instance = MyClass(np.array([1, 2, 3])) + checkpoint_manager.save(1, instance) + # Creates a copy of instance + restored_instance = checkpoint_manager.restore(1, instance) + + # If you don't want to create a copy: + instance.from_state_dict(checkpoint_manager.restore(1)) + ``` +""" + +import copy +import functools +import typing +from typing import Any, Dict, Generic, Optional, Protocol, TypeVar, Union + +from dopamine.jax import serialization +from etils import epath +import msgpack +from orbax import checkpoint + + +@typing.runtime_checkable +class Checkpointable(Protocol): + """Checkpointable protocol. Must implement to_state_dict, from_state_dict.""" + + def to_state_dict(self) -> Dict[str, Any]: + ... + + def from_state_dict(self, state_dict: Dict[str, Any]) -> None: + ... + + +CheckpointableT = TypeVar('CheckpointableT', bound=Checkpointable) + + +class CheckpointHandler(checkpoint.CheckpointHandler, Generic[CheckpointableT]): + """Checkpointable protocol checkpoint handler.""" + + def __init__(self, filename: str = 'checkpoint.msgpack') -> None: + self._filename = filename + + def save(self, directory: epath.Path, item: CheckpointableT) -> None: + if not isinstance(item, Checkpointable): + raise NotImplementedError(f'Item {item!r} must implement Checkpointable') + directory.mkdir(exist_ok=True, parents=True) + filename = directory / self._filename + + # Get bytes using MsgPack + packed = msgpack.packb( + item.to_state_dict(), + default=serialization.encode, + strict_types=False, + use_bin_type=True, + ) + filename.write_bytes(packed) + + @typing.overload + def restore( + self, directory: epath.Path, item: CheckpointableT + ) -> CheckpointableT: + ... + + @typing.overload + def restore(self, directory: epath.Path, item: None = None) -> Dict[str, Any]: + ... + + def restore( + self, directory: epath.Path, item: Optional[CheckpointableT] = None + ) -> Union[CheckpointableT, Dict[str, Any]]: + filename = directory / self._filename + state_dict = msgpack.unpackb( + filename.read_bytes(), + object_hook=serialization.decode, + raw=False, + strict_map_key=False, + ) + + if item is None: + return state_dict + + item = copy.deepcopy(item) + item.from_state_dict(state_dict) + return item + + def structure(self, directory: epath.Path) -> None: + return None + + +# pylint: disable=g-long-lambda +# Orbax requires a `Checkpointer` object. This wraps a `CheckpointHandler`. +# To make it easier for end-users we'll supply a `Checkpointer` that already +# performs this instantiation. +Checkpointer = functools.partial(checkpoint.Checkpointer, CheckpointHandler()) +# pylint: enable=g-long-lambda diff --git a/dopamine/jax/continuous_networks.py b/dopamine/jax/continuous_networks.py index 728d8e84..0bd9f07c 100644 --- a/dopamine/jax/continuous_networks.py +++ b/dopamine/jax/continuous_networks.py @@ -14,6 +14,7 @@ # limitations under the License. """Networks for continuous control agents.""" +from collections.abc import Callable import functools import operator from typing import NamedTuple, Optional, Tuple @@ -22,33 +23,53 @@ import gin import jax from jax import numpy as jnp -import tensorflow as tf from tensorflow_probability.substrates import jax as tfp tfd = tfp.distributions tfb = tfp.bijectors -class SacActorOutput(NamedTuple): - """The output of a SAC actor.""" +class ActorOutput(NamedTuple): + """The output of a continuous actor.""" mean_action: jnp.ndarray sampled_action: jnp.ndarray log_probability: jnp.ndarray -class SacCriticOutput(NamedTuple): - """The output of a SAC critic.""" +class CriticOutput(NamedTuple): + """The output of a continuous critic.""" q_value1: jnp.ndarray q_value2: jnp.ndarray -class SacOutput(NamedTuple): - """The output of a SACNetwork, including the actor and critic outputs.""" +class ActorCriticOutput(NamedTuple): + """The output of the actor critic network.""" - actor: SacActorOutput - critic: SacCriticOutput + actor: ActorOutput + critic: CriticOutput + + +class PPOActorOutput(NamedTuple): + """The output of a continuous ppo actor.""" + + sampled_action: jnp.ndarray + log_probability: jnp.ndarray + entropy: jnp.ndarray + + +class PPOCriticOutput(NamedTuple): + """The output of a continuous ppo critic.""" + + q_value: jnp.ndarray + + +class PPOActorCriticOutput(NamedTuple): + """The output of the ppo actor critic network.""" + + actor: PPOActorOutput + critic: PPOCriticOutput class _Tanh(tfb.Tanh): @@ -56,7 +77,7 @@ class _Tanh(tfb.Tanh): def _inverse(self, y): # We perform clipping in the _inverse function, as is done in TF-Agents. y = jnp.where( - jnp.less_equal(jnp.abs(y), 1.0), tf.clip(y, -0.99999997, 0.99999997), y + jnp.less_equal(jnp.abs(y), 1.0), jnp.clip(y, -0.99999997, 0.99999997), y ) return jnp.arctanh(y) @@ -89,15 +110,92 @@ def init(key, shape, dtype=dtype): return init -class SACCriticNetwork(nn.Module): - """A simple critic network used in SAC.""" +@gin.configurable +def create_activation( + activation='relu', +): + """Create an activation function for the network.""" + if activation == 'relu': + return nn.relu + elif activation == 'tanh': + return nn.tanh + else: + raise ValueError('Unsupported activation {}'.format(activation)) + +class ActorNetwork(nn.Module): + """A simple continuous actor network.""" + + action_shape: Tuple[int, ...] num_layers: int = 2 hidden_units: int = 256 + action_limits: Optional[Tuple[Tuple[float, ...], Tuple[float, ...]]] = None + activation: Callable[[jax.Array], jax.Array] = nn.relu + kernel_initializer: jax.nn.initializers.Initializer = ( + jax.nn.initializers.glorot_uniform() + ) + + @nn.compact + def __call__( + self, + state: jnp.ndarray, + key: Optional[jnp.ndarray] = None, + action: Optional[jnp.ndarray] = None, + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + action_dim = functools.reduce(operator.mul, self.action_shape, 1) + + # Preprocess inputs + x = state.astype(jnp.float32) + x = x.reshape(-1) # flatten + + for _ in range(self.num_layers): + x = nn.Dense( + features=self.hidden_units, kernel_init=self.kernel_initializer + )(x) + x = self.activation(x) + + # Note we are only producing a diagonal covariance matrix, not a full + # covariance matrix as it is difficult to ensure that it would be PSD. + loc_and_scale_diag = nn.Dense( + features=action_dim * 2, kernel_init=self.kernel_initializer + )(x) + loc, scale_diag = jnp.split(loc_and_scale_diag, 2) + # Exponentiate to only get positive terms. + scale_diag = jnp.exp(scale_diag) + dist = tfd.MultivariateNormalDiag(loc=loc, scale_diag=scale_diag) + if self.action_limits is None: + mode = dist.mode() + else: + lower_action_limit = jnp.asarray(self.action_limits[0], dtype=jnp.float32) + upper_action_limit = jnp.asarray(self.action_limits[1], dtype=jnp.float32) + mean = (lower_action_limit + upper_action_limit) / 2.0 + magnitude = (upper_action_limit - lower_action_limit) / 2.0 + mode = magnitude * jnp.tanh(dist.mode()) + mean + dist = _transform_distribution(dist, mean, magnitude) + if action is None: + if key is None: + raise ValueError('Key must be provided if action is None.') + action = dist.sample(seed=key) + action_probability = dist.log_prob(action) + + mode = jnp.reshape(mode, self.action_shape) + action = jnp.reshape(action, self.action_shape) + + return mode, action, action_probability + + +class CriticNetwork(nn.Module): + """A simple continuous critic network.""" + + num_layers: int = 2 + hidden_units: int = 256 + activation: Callable[[jax.Array], jax.Array] = nn.relu + kernel_initializer: jax.nn.initializers.Initializer = ( + jax.nn.initializers.glorot_uniform() + ) @nn.compact def __call__(self, state: jnp.ndarray, action: jnp.ndarray) -> jnp.ndarray: - kernel_initializer = jax.nn.initializers.glorot_uniform() # Preprocess inputs a = action.reshape(-1) # flatten @@ -106,45 +204,53 @@ def __call__(self, state: jnp.ndarray, action: jnp.ndarray) -> jnp.ndarray: x = jnp.concatenate((x, a)) for _ in range(self.num_layers): - x = nn.Dense(features=self.hidden_units, kernel_init=kernel_initializer)( - x - ) - x = nn.relu(x) + x = nn.Dense( + features=self.hidden_units, kernel_init=self.kernel_initializer + )(x) + x = self.activation(x) - return nn.Dense(features=1, kernel_init=kernel_initializer)(x) + return nn.Dense(features=1, kernel_init=self.kernel_initializer)(x) @gin.configurable -class SACNetwork(nn.Module): - """Non-convolutional value and policy networks for SAC.""" +class ActorCriticNetwork(nn.Module): + """Non-convolutional actor critic value and policy networks.""" action_shape: Tuple[int, ...] num_layers: int = 2 hidden_units: int = 256 action_limits: Optional[Tuple[Tuple[float, ...], Tuple[float, ...]]] = None + activation: Callable[[jax.Array], jax.Array] = nn.relu + kernel_initializer: jax.nn.initializers.Initializer = ( + jax.nn.initializers.glorot_uniform() + ) def setup(self): - action_dim = functools.reduce(operator.mul, self.action_shape, 1) - - # The setting of these initializers were borrowed from the TF-Agents SAC - # implementation. - kernel_initializer = jax.nn.initializers.glorot_uniform() - - self._critic1 = SACCriticNetwork(self.num_layers, self.hidden_units) - self._critic2 = SACCriticNetwork(self.num_layers, self.hidden_units) - - self._actor_layers = [ - nn.Dense(features=self.hidden_units, kernel_init=kernel_initializer) - for _ in range(self.num_layers) - ] - self._actor_final_layer = nn.Dense( - features=action_dim * 2, kernel_init=kernel_initializer + self._actor = ActorNetwork( + self.action_shape, + self.num_layers, + self.hidden_units, + self.action_limits, + self.activation, + self.kernel_initializer, + ) + self._critic1 = CriticNetwork( + self.num_layers, + self.hidden_units, + self.activation, + self.kernel_initializer, + ) + self._critic2 = CriticNetwork( + self.num_layers, + self.hidden_units, + self.activation, + self.kernel_initializer, ) def __call__( self, state: jnp.ndarray, key: jnp.ndarray, mean_action: bool = True - ) -> SacOutput: - """Calls the SAC actor/critic networks. + ) -> ActorCriticOutput: + """Calls the actor/critic networks. This has two important purposes: 1. It is used to initialize all parameters of both networks. @@ -168,10 +274,10 @@ def __call__( else: critic_output = self.critic(state, actor_output.sampled_action) - return SacOutput(actor_output, critic_output) + return ActorCriticOutput(actor_output, critic_output) - def actor(self, state: jnp.ndarray, key: jnp.ndarray) -> SacActorOutput: - """Calls the SAC actor network. + def actor(self, state: jnp.ndarray, key: jnp.ndarray) -> ActorOutput: + """Calls the actor network. This can be called using network_def.apply(..., method=network_def.actor). @@ -184,51 +290,206 @@ def actor(self, state: jnp.ndarray, key: jnp.ndarray) -> SacActorOutput: A named tuple containing a sampled action, the mean action, and the likelihood of the sampled action. """ + return ActorOutput(*self._actor(state, key=key)) + + def critic(self, state: jnp.ndarray, action: jnp.ndarray) -> CriticOutput: + """Calls the critic network. + + SAC uses two Q networks to reduce overestimation bias. + This can be called using network_def.apply(..., method=network_def.critic). + + Args: + state: An input state. + action: An action to compute the value of. + + Returns: + A named tuple containing the Q values of each network. + """ + return CriticOutput( + self._critic1(state, action), self._critic2(state, action) + ) + + +@gin.configurable +class PPOActorNetwork(nn.Module): + """Actor network for PPO. + + Attributes: + action_shape: shape of the action. + num_layers: number of layers in the network. + hidden_units: number of hidden units in the network. + activation: activation function to use in the network. + """ + + action_shape: Tuple[int, ...] + num_layers: int = 2 + hidden_units: int = 64 + action_limits: Optional[Tuple[Tuple[float, ...], Tuple[float, ...]]] = None + activation: Callable[[jax.Array], jax.Array] = nn.tanh + + @nn.compact + def __call__( + self, + state: jnp.ndarray, + key: Optional[jnp.ndarray] = None, + action: Optional[jnp.ndarray] = None, + ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: + action_dim = functools.reduce(operator.mul, self.action_shape, 1) + # Preprocess inputs x = state.astype(jnp.float32) x = x.reshape(-1) # flatten - for layer in self._actor_layers: - x = layer(x) - x = nn.relu(x) - - # Note we are only producing a diagonal covariance matrix, not a full - # covariance matrix as it is difficult to ensure that it would be PSD. - loc_and_scale_diag = self._actor_final_layer(x) - loc, scale_diag = jnp.split(loc_and_scale_diag, 2) - # Exponentiate to only get positive terms. + for _ in range(self.num_layers): + x = nn.Dense( + features=self.hidden_units, + kernel_init=nn.initializers.orthogonal(jnp.sqrt(2.0)), + )(x) + x = self.activation(x) + loc = nn.Dense( + features=action_dim, + kernel_init=nn.initializers.orthogonal(jnp.sqrt(0.01)), + )(x) + # scale_diag is state independent and initialized to zero. + scale_diag = nn.Dense( + features=action_dim, kernel_init=nn.initializers.zeros + )(jnp.ones_like(x)) scale_diag = jnp.exp(scale_diag) - dist = tfd.MultivariateNormalDiag(loc=loc, scale_diag=scale_diag) - if self.action_limits is None: - mode = dist.mode() - else: + + dist = tfd.Normal(loc=loc, scale=scale_diag) + + # Compute the entropy before transforming, as the transformation does not + # have a constant Jacobian. + entropy = jnp.sum(dist.entropy()) + if self.action_limits is not None: lower_action_limit = jnp.asarray(self.action_limits[0], dtype=jnp.float32) upper_action_limit = jnp.asarray(self.action_limits[1], dtype=jnp.float32) mean = (lower_action_limit + upper_action_limit) / 2.0 magnitude = (upper_action_limit - lower_action_limit) / 2.0 - mode = magnitude * jnp.tanh(dist.mode()) + mean dist = _transform_distribution(dist, mean, magnitude) - sampled_action = dist.sample(seed=key) - action_probability = dist.log_prob(sampled_action) - mode = jnp.reshape(mode, self.action_shape) - sampled_action = jnp.reshape(sampled_action, self.action_shape) + if action is None: + if key is None: + raise ValueError('Key must be provided if action is None.') + action = dist.sample(seed=key) - return SacActorOutput(mode, sampled_action, action_probability) + log_probability = jnp.sum(dist.log_prob(action)) - def critic(self, state: jnp.ndarray, action: jnp.ndarray) -> SacCriticOutput: - """Calls the SAC critic network. + action = jnp.reshape(action, self.action_shape) + + return action, log_probability, entropy + + +class PPOCriticNetwork(nn.Module): + """Critic network for PPO. + + Attributes: + num_layers: number of layers in the network. + hidden_units: number of hidden units in the network. + activation: activation function to use in the network. + """ + + num_layers: int = 2 + hidden_units: int = 64 + activation: Callable[[jax.Array], jax.Array] = nn.tanh + + @nn.compact + def __call__(self, state: jnp.ndarray) -> jnp.ndarray: + # Preprocess inputs + x = state.astype(jnp.float32) + x = x.reshape(-1) # flatten + + for _ in range(self.num_layers): + x = nn.Dense( + features=self.hidden_units, + kernel_init=nn.initializers.orthogonal(jnp.sqrt(2.0)), + )(x) + x = self.activation(x) + return nn.Dense(features=1, kernel_init=nn.initializers.orthogonal(1.0))(x) + + +@gin.configurable +class PPOActorCriticNetwork(nn.Module): + """Actor critic network for PPO. + + Attributes: + action_shape: shape of the action. + num_layers: number of layers in the networks. + hidden_units: number of hidden units in the networks. + activation: activation function to use in the networks. + """ + + action_shape: Tuple[int, ...] + num_layers: int = 2 + hidden_units: int = 64 + action_limits: Optional[Tuple[Tuple[float, ...], Tuple[float, ...]]] = None + activation: Callable[[jax.Array], jax.Array] = nn.tanh + + def setup(self): + self._actor = PPOActorNetwork( + self.action_shape, + self.num_layers, + self.hidden_units, + self.action_limits, + self.activation, + ) + self._critic = PPOCriticNetwork( + self.num_layers, self.hidden_units, self.activation + ) + + def __call__( + self, state: jnp.ndarray, key: jnp.ndarray + ) -> PPOActorCriticOutput: + """Calls the actor/critic networks. + + This has two important purposes: + 1. It is used to initialize all parameters of both networks. + 2. It is used to efficiently calculate the outputs of both the actor + and critic networks on a single input. + + Args: + state: An input state. + key: A PRNGKey to use to sample an action from the actor's output + distribution. + + Returns: + A named tuple containing the outputs from both networks. + """ + actor_output = self.actor(state, key) + critic_output = self.critic(state) + return PPOActorCriticOutput(actor_output, critic_output) + + def actor( + self, + state: jnp.ndarray, + key: Optional[jnp.ndarray] = None, + action: Optional[jnp.ndarray] = None, + ) -> PPOActorOutput: + """Calls the actor network. + + This can be called using network_def.apply(..., method=network_def.actor). + + Args: + state: An input state. + key: A PRNGKey to use to sample an action from the actor's output + distribution. + action: An action to use to feed to the actor network. + + Returns: + A named tuple containing the provided action, the likelihood of the + provided action, and the entropy of the action. + """ + return PPOActorOutput(*self._actor(state, key, action)) + + def critic(self, state: jnp.ndarray) -> PPOCriticOutput: + """Calls the critic network. - SAC uses two Q networks to reduce overestimation bias. This can be called using network_def.apply(..., method=network_def.critic). Args: state: An input state. - action: An action to compute the value of. Returns: - A named tuple containing the Q values of each network. + A named tuple containing the Q value of the network. """ - return SacCriticOutput( - self._critic1(state, action), self._critic2(state, action) - ) + return PPOCriticOutput(self._critic(state)) diff --git a/dopamine/jax/networks.py b/dopamine/jax/networks.py index b9e8f02c..373427fc 100644 --- a/dopamine/jax/networks.py +++ b/dopamine/jax/networks.py @@ -14,16 +14,21 @@ # limitations under the License. """Various networks for Jax Dopamine agents.""" +import functools import itertools +import operator import time from typing import Optional, Sequence, Tuple, Union + from absl import logging from dopamine.discrete_domains import atari_lib +from dopamine.jax import continuous_networks from flax import linen as nn import gin import jax import jax.numpy as jnp import numpy as onp +from tensorflow_probability.substrates import jax as tfp gin.constant('jax_networks.CARTPOLE_OBSERVATION_DTYPE', jnp.float64) @@ -599,3 +604,107 @@ def __call__(self, x, support, eval_mode=False, key=None): return atari_lib.RainbowNetworkType(q_values, logits, probabilities) q_values = jnp.sum(logits, axis=1) # Sum over all the num_atoms return atari_lib.DQNNetworkType(q_values) + + +@gin.configurable +class PPOSharedNetwork(nn.Module): + """Shared network for PPO actor and critic.""" + + inputs_preprocessed: bool = False + + @nn.compact + def __call__(self, x) -> jnp.ndarray: + initializer = nn.initializers.orthogonal(jnp.sqrt(2)) + if not self.inputs_preprocessed: + x = preprocess_atari_inputs(x) + x = nn.Conv( + features=32, + kernel_size=(8, 8), + strides=4, + padding='VALID', + kernel_init=initializer, + )(x) + x = nn.relu(x) + x = nn.Conv( + features=64, + kernel_size=(4, 4), + strides=2, + padding='VALID', + kernel_init=initializer, + )(x) + x = nn.relu(x) + x = nn.Conv( + features=64, + kernel_size=(3, 3), + strides=1, + padding='VALID', + kernel_init=initializer, + )(x) + x = nn.relu(x) + x = x.reshape((-1)) # flatten + x = nn.Dense(features=512, kernel_init=initializer)(x) + x = nn.relu(x) + return x + + +@gin.configurable +class PPOActorNetwork(nn.Module): + """Actor backbone for PPO.""" + + num_actions: int + + @nn.compact + def __call__(self, x) -> jnp.ndarray: + initializer = nn.initializers.orthogonal(0.01) + return nn.Dense(features=self.num_actions, kernel_init=initializer)(x) + + +class PPOCriticNetwork(nn.Module): + """Critic backbone for PPO.""" + + @nn.compact + def __call__(self, x) -> jnp.ndarray: + initializer = nn.initializers.orthogonal(1.0) + return nn.Dense(features=1, kernel_init=initializer)(x) + + +@gin.configurable +class PPODiscreteActorCriticNetwork(nn.Module): + """Convolutional actor critic value and policy networks.""" + + action_shape: Tuple[int, ...] + inputs_preprocessed: bool = False + + def setup(self): + action_dim = functools.reduce(operator.mul, self.action_shape, 1) + self._shared_network = PPOSharedNetwork(self.inputs_preprocessed) + self._actor = PPOActorNetwork(action_dim) + self._critic = PPOCriticNetwork() + + def __call__( + self, state: jnp.ndarray, key: jnp.ndarray + ) -> continuous_networks.ActorCriticOutput: + actor_output = self.actor(state, key) + critic_output = self.critic(state) + return continuous_networks.PPOActorCriticOutput(actor_output, critic_output) + + def actor( + self, + state: jnp.ndarray, + key: Optional[jnp.ndarray] = None, + action: Optional[jnp.ndarray] = None, + ) -> continuous_networks.PPOActorOutput: + logits = self._actor(self._shared_network(state)) + dist = tfp.distributions.Categorical(logits=logits) + if action is None: + if key is None: + raise ValueError('Key must be provided if action is None.') + action = dist.sample(seed=key) + log_probability = dist.log_prob(action) + entropy = dist.entropy() + return continuous_networks.PPOActorOutput(action, log_probability, entropy) + + def critic(self, state: jnp.ndarray) -> continuous_networks.PPOCriticOutput: + return continuous_networks.PPOCriticOutput( + self._critic(self._shared_network(state)) + ) diff --git a/dopamine/agents/__init__.py b/dopamine/jax/replay_memory/__init__.py similarity index 100% rename from dopamine/agents/__init__.py rename to dopamine/jax/replay_memory/__init__.py diff --git a/dopamine/jax/replay_memory/accumulator.py b/dopamine/jax/replay_memory/accumulator.py new file mode 100644 index 00000000..76207c06 --- /dev/null +++ b/dopamine/jax/replay_memory/accumulator.py @@ -0,0 +1,214 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""An accumulator used by the Jax replay buffer.""" +import collections +import pickle +import typing +from typing import Any, Iterable, Protocol +from typing import TypeVar + +from dopamine.jax import checkpointers +from dopamine.jax.replay_memory import elements +import jax +import numpy as np + + +ReplayElementT = TypeVar('ReplayElementT', bound=elements.ReplayElementProtocol) + + +@typing.runtime_checkable +class Accumulator(checkpointers.Checkpointable, Protocol[ReplayElementT]): + + def accumulate( + self, transition: elements.TransitionElement + ) -> Iterable[ReplayElementT]: + ... + + def clear(self) -> None: + ... + + +class TransitionAccumulator(Accumulator[elements.ReplayElement]): + """A transition accumulator used for preparing elements for replay. + + This class will consume raw transitions and prepare them for storing in the + replay buffer. Specifically, it will only return validly stacked frames, and + will only return valid n-step returns. + This enables us to guarantee that the replay buffer only contains valid + elements to sample. + """ + + def __init__( + self, + stack_size: int, + update_horizon: int, + gamma: float, + ): + assert update_horizon > 0, 'update_horizon must be positive.' + assert stack_size > 0, 'stack_size must be positive.' + + self._stack_size = stack_size + self._update_horizon = update_horizon + self._gamma = gamma + + self._trajectory = collections.deque[elements.TransitionElement]( + maxlen=self._update_horizon + self._stack_size + ) + + def _make_replay_element(self) -> elements.ReplayElement | None: + trajectory_len = len(self._trajectory) + + last_transition = self._trajectory[-1] + # Check if we have a valid transition, i.e. we either + # 1) have accumulated more transitions than the update horizon + # 2) have a trajectory shorter than the update horizon, but the + # last element is terminal + if not ( + trajectory_len > self._update_horizon + or (trajectory_len > 1 and last_transition.is_terminal) + ): + return None + + # Calculate effective horizon, this can differ from the update horizon + # when we have n-step transitions where the last observation is terminal. + effective_horizon = self._update_horizon + if last_transition.is_terminal and trajectory_len <= self._update_horizon: + effective_horizon = trajectory_len - 1 + + # pytype: disable=attribute-error + observation_shape = last_transition.observation.shape + (self._stack_size,) + observation_dtype = last_transition.observation.dtype + # pytype: enable=attribute-error + + o_tm1 = np.zeros(observation_shape, observation_dtype) + # Initialize the slice for which this observation is valid. + # The start index for o_tm1 is the start of the n-step trajectory. + # The end index for o_tm1 is just moving over `stack size`. + o_tm1_slice = slice( + trajectory_len - effective_horizon - self._stack_size, + trajectory_len - effective_horizon - 1, + ) + # The action chosen will be the last transition in the stack. + a_tm1 = self._trajectory[o_tm1_slice.stop].action + + o_t = np.zeros(observation_shape, observation_dtype) + # Initialize the slice for which this observation is valid. + # The start index for o_t is just moving backwards `stack size`. + # The end index for o_t is just the last index of the n-step trajectory. + o_t_slice = slice( + trajectory_len - self._stack_size, + trajectory_len - 1, + ) + # Terminal information will come from the last transition in the stack + is_terminal = self._trajectory[o_t_slice.stop].is_terminal + episode_end = self._trajectory[o_t_slice.stop].is_terminal + + # Slice to accumulate n-step returns. This will be the end + # transition of o_tm1 plus the effective horizon. + # This might over-run the trajectory length in the case of n-step + # returns where the last transition is terminal. + gamma_slice = slice( + o_tm1_slice.stop, + o_tm1_slice.stop + self._update_horizon - 1, + ) + assert o_t_slice.stop - o_tm1_slice.stop == effective_horizon + assert o_t_slice.stop - 1 >= o_tm1_slice.stop + + # Now we'll iterate through the n-step trajectory and compute the + # cumulant and insert the observations into the appropriate stacks + r_t = 0.0 + for t, transition_t in enumerate(self._trajectory): + # If we should be accumulating reward for an n-step return? + if gamma_slice.start <= t <= gamma_slice.stop: + r_t += transition_t.reward * (self._gamma ** (t - gamma_slice.start)) + + # If we should be accumulating frames for the frame-stack? + if o_tm1_slice.start <= t <= o_tm1_slice.stop: + o_tm1[..., t - o_tm1_slice.start] = transition_t.observation + if o_t_slice.start <= t <= o_t_slice.stop: + o_t[..., t - o_t_slice.start] = transition_t.observation + + return elements.ReplayElement( + state=o_tm1, + action=a_tm1, + reward=r_t, + next_state=o_t, + is_terminal=is_terminal, + episode_end=episode_end, + ) + + def accumulate( + self, transition: elements.TransitionElement + ) -> Iterable[elements.ReplayElement]: + """Add a transition to the accumulator, maybe receive valid ReplayElements. + + If the transition has a terminal or end of episode signal, it will create a + new trajectory. + + If the transition is terminal the iterator will yield multiple elements + corresponding to n-step returns with n ∊ [1, n]. + + Args: + transition: TransitionElement to add. + + Yields: + A ReplayElement if there is a valid transition. + """ + self._trajectory.append(transition) + + # If this transition terminated a trajectory we'll yield + # as many replay elements as we can before clearing the trajectory + if transition.is_terminal: + while replay_element := self._make_replay_element(): + yield replay_element + self._trajectory.popleft() + self._trajectory.clear() + else: + # Attempt to yield a replay element + if replay_element := self._make_replay_element(): + yield replay_element + # If the transition truncates the trajectory then clear it + # We don't yield n-1, n-2, ..., 1 step returns as + # this transition is not terminal. + if transition.episode_end: + self._trajectory.clear() + + def clear(self) -> None: + """Clear the accumulator.""" + self._trajectory.clear() + + def to_state_dict(self) -> dict[str, Any]: + """Serialize to a state dictionary.""" + # We'll serialize each transition as a flat representation of its PyTree. + # This will encode the transitions as: + # [[action, reward, is_terminal, episode_end], ...] + steps = iter(self._trajectory) + leaves, treedef = jax.tree_util.tree_flatten(next(steps, None)) + steps = [] if not leaves else [leaves, *map(treedef.flatten_up_to, steps)] + return {'trajectory': steps, 'treedef': pickle.dumps(treedef)} + + def from_state_dict(self, state_dict: dict[str, Any]) -> None: + """Mutate accumulator with data from `state_dict`.""" + # Load the treedef we flattened with + treedef: jax.tree_util.PyTreeDef = pickle.loads(state_dict['treedef']) + + # We can now unflatten each transition + # It's encoded as: + # [[action, reward, is_terminal, episode_end], ...] + # After this transformation we'll have: + # [TransitionElement(None, action, reward, is_terminal, episode_end), ...] + transitions = map(treedef.unflatten, state_dict['trajectory']) + self._trajectory.clear() + self._trajectory.extend(transitions) diff --git a/dopamine/jax/replay_memory/elements.py b/dopamine/jax/replay_memory/elements.py new file mode 100644 index 00000000..89dd44ee --- /dev/null +++ b/dopamine/jax/replay_memory/elements.py @@ -0,0 +1,139 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Various containers used by the replay buffer code.""" + +import typing +from typing import Any, NewType, Optional, Protocol + +from flax import struct +import numpy as np +import numpy.typing as npt +import snappy + +ReplayItemID = NewType('ReplayItemID', int) + + +class TransitionElement(typing.NamedTuple): + observation: Optional[npt.NDArray[Any]] + action: int + reward: float + is_terminal: bool + episode_end: bool = False + + +@typing.runtime_checkable +class ReplayElementProtocol(Protocol): + + def pack(self) -> 'ReplayElementProtocol': + ... + + def unpack(self) -> 'ReplayElementProtocol': + ... + + @property + def is_compressed(self) -> bool: + ... + + +def compress(buffer: npt.NDArray) -> npt.NDArray: + """Compress a numpy array using snappy. + + Args: + buffer: npt.NDArray, buffer to compress. + + Returns: + Numpy structured array consisting of the following fields: + data: compressed data in bytes + shape: the shape of the uncompressed array + dtype: a string representation of the dtype + """ + # Compress the numpy array using snappy + if not buffer.flags['C_CONTIGUOUS']: + buffer = buffer.copy(order='C') + compressed = np.frombuffer(snappy.compress(buffer), dtype=np.uint8) + # Construct the numpy structured array + return np.array( + (compressed, buffer.shape, buffer.dtype.str), + dtype=[ + ('data', 'u1', compressed.shape), + ('shape', 'i4', (len(buffer.shape),)), + ('dtype', f'S{len(buffer.dtype.str)}'), + ], + ) + + +def uncompress(compressed: npt.NDArray) -> npt.NDArray: + """Uncompress a numpy array that has been compressed via `compress`. + + Args: + compressed: npt.NDArray, numpy structured array with data, shape, and dtype. + + Returns: + Uncompressed npt.NDArray + """ + # Create shape tuple + shape = tuple(compressed['shape']) + # Get the dtype string + dtype = compressed['dtype'].item() + # Get the compressed bytes and uncompress them using snappy + compressed_bytes = compressed['data'].tobytes() + uncompressed = snappy.uncompress(compressed_bytes) + # Construct the numpy array + return np.ndarray(shape=shape, dtype=dtype, buffer=uncompressed) + + +class ReplayElement(ReplayElementProtocol, struct.PyTreeNode): + """A single replay transition element supporting compression.""" + + state: npt.NDArray[np.float64] + action: npt.NDArray[np.int_] | npt.NDArray[np.float64] | int + reward: npt.NDArray[np.float64] | float + next_state: npt.NDArray[np.float64] + is_terminal: npt.NDArray[np.bool_] | bool + episode_end: npt.NDArray[np.bool_] | bool + + def pack(self) -> 'ReplayElement': + # NOTE: pytype has a problem subclassing generics. + # This should be solved in Py311 with `typing.Self` + # pytype: disable=attribute-error + return self.replace( + state=compress(self.state), + next_state=compress(self.next_state), + ) + # pytype: enable=attribute-error + + def unpack(self) -> 'ReplayElement': + # NOTE: pytype has a problem subclassing generics. + # This should be solved in Py311 with `typing.Self` + # pytype: disable=attribute-error + return self.replace( + state=uncompress(self.state), + next_state=uncompress(self.next_state), + ) + # pytype: enable=attribute-error + + @property + def is_compressed(self) -> bool: + # NOTE: pytype has a problem subclassing generics. + # This should be solved in Py311 with `typing.Self` + # pytype: disable=attribute-error + # As per the numpy documentation the recommended way to check for a + # structured array is to check if `dtype.names is not None`. + # See: https://numpy.org/doc/stable/user/basics.rec.html + return ( + self.state.dtype.names is not None + and self.next_state.dtype.names is not None + ) + # pytype: enable=attribute-error diff --git a/dopamine/jax/replay_memory/replay_buffer.py b/dopamine/jax/replay_memory/replay_buffer.py new file mode 100644 index 00000000..5a6a9bc7 --- /dev/null +++ b/dopamine/jax/replay_memory/replay_buffer.py @@ -0,0 +1,269 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Simpler implementation of the standard DQN replay memory.""" +import collections +import functools +import operator +import pickle +import typing +from typing import Any, Generic, Literal, TypeVar + +from dopamine.jax import checkpointers +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import samplers +import gin +import jax +import numpy as np +import numpy.typing as npt +from orbax import checkpoint as orbax + +ReplayItemID = elements.ReplayItemID +ReplayElementT = TypeVar('ReplayElementT', bound=elements.ReplayElementProtocol) + + +@gin.configurable +class ReplayBuffer(checkpointers.Checkpointable, Generic[ReplayElementT]): + """A Jax re-implementation of the Dopamine Replay Buffer. + + Stores transitions, state, action, reward, next_state, terminal (and any + extra contents specified) in a circular buffer and provides a uniform + sampling mechanism. + + The main changes from the original Dopamine implementation are: + - The original Dopamine replay buffer stored the raw observations and stacked + them upon sampling. Although space efficient, this is time inefficient. We + use the same compression mechanism used by dqn_zoo to store only stacked + states in a space efficient manner. + - Similarly, n-step transitions were computed at sample time. We only store + n-step returns in the buffer by using an Accumulator (which is also used for + stacking). + - The above two points allow us to sample uniformly from the FIFO queue, + without needing to determine invalid ranges (as in the original replay + buffer). The computation of invalid ranges was inefficient, as it required + verifying each sampled index, and potentially resampling them if they fell + in an invalid range. This computation scales linearly with the batch size, + and is thus quite inefficient. + - The original Dopamine replay buffer maintained a static array and performed + modulo arithmetic when adding/sampling. This is unnecessarily complicated + and we can achieve the same result by maintaining a FIFO queue (using an + OrderedDict structure) containing only valid transitions. + """ + + def __init__( + self, + transition_accumulator: accumulator.Accumulator[ReplayElementT], + sampling_distribution: samplers.SamplingDistribution, + *, + batch_size: int, + max_capacity: int, + checkpoint_duration: int = 4, + compress: bool = True, + ): + """Initializes the ReplayBuffer.""" + self.add_count = 0 + self._max_capacity = max_capacity + self._compress = compress + self._memory = collections.OrderedDict[ReplayItemID, ReplayElementT]() + + self._transition_accumulator = transition_accumulator + self._sampling_distribution = sampling_distribution + + if checkpoint_duration < 1: + raise ValueError(f'Invalid checkpoint_duration: {checkpoint_duration}') + + self._checkpoint_duration = checkpoint_duration + self._batch_size = batch_size + + def add(self, transition: elements.TransitionElement, **kwargs: Any) -> None: + """Add a transition to the replay buffer.""" + for replay_element in self._transition_accumulator.accumulate(transition): + if self._compress: + replay_element = replay_element.pack() + + # Add replay element to memory + key = ReplayItemID(self.add_count) + self._memory[key] = replay_element + self._sampling_distribution.add(key, **kwargs) + self.add_count += 1 + # If we're beyond our capacity... + if self.add_count > self._max_capacity: + # Pop the oldest item from memory and keep the key + # so we can ask the sampling distribution to remove it + oldest_key, _ = self._memory.popitem(last=False) + self._sampling_distribution.remove(oldest_key) + + @typing.overload + def sample( + self, + size: int | None = None, + *, + with_sample_metadata: Literal[False] = False, + ) -> ReplayElementT: + ... + + @typing.overload + def sample( + self, + size: int, + *, + with_sample_metadata: Literal[True] = True, + ) -> tuple[ReplayElementT, samplers.SampleMetadata]: + ... + + def sample( + self, + size: int | None = None, + *, + with_sample_metadata: bool = False, + ) -> ReplayElementT | tuple[ReplayElementT, samplers.SampleMetadata]: + """Sample a batch of elements from the replay buffer.""" + if self.add_count < 1: + raise ValueError('No samples in replay buffer!') + if size is None: + size = self._batch_size + if size < 1: + raise ValueError(f'Invalid size: {size}, size must be >= 1.') + + samples = self._sampling_distribution.sample(size) + replay_elements = operator.itemgetter(*samples.keys)(self._memory) + if not isinstance(replay_elements, tuple): + # When size == 1, replay_elements will hold a single ReplayElement, as + # opposed to a tuple of ReplayElements (which is what is expected in the + # call to tree_map below). We fix this by forcing replay_elements into a + # tuple, if necessary. + replay_elements = (replay_elements,) + if self._compress: + replay_elements = map(operator.methodcaller('unpack'), replay_elements) + + batch = jax.tree_util.tree_map(lambda *xs: np.stack(xs), *replay_elements) + return (batch, samples) if with_sample_metadata else batch + + @typing.overload + def update(self, keys: npt.NDArray[ReplayItemID], **kwargs: Any) -> None: + ... + + @typing.overload + def update(self, keys: ReplayItemID, **kwargs: Any) -> None: + ... + + def update( + self, + keys: npt.NDArray[ReplayItemID] | ReplayItemID, + **kwargs: Any, + ) -> None: + self._sampling_distribution.update(keys, **kwargs) + + def clear(self) -> None: + """Clear the replay buffer.""" + self.add_count = 0 + self._memory.clear() + self._transition_accumulator.clear() + self._sampling_distribution.clear() + + def to_state_dict(self) -> dict[str, Any]: + """Serialize replay buffer to a state dictionary.""" + # Serialize memory. We'll serialize keys and values separately. + keys = list(self._memory.keys()) + # To serialize values we'll flatten each transition element. + # This will serialize replay elements as: + # [[state, action, reward, next_state, is_terminal, episode_end], ...] + values = iter(self._memory.values()) + leaves, treedef = jax.tree_util.tree_flatten(next(values, None)) + values = [] if not leaves else [leaves, *map(treedef.flatten_up_to, values)] + + return { + 'add_count': self.add_count, + 'memory': { + 'keys': keys, + 'values': values, + 'treedef': pickle.dumps(treedef), + }, + 'sampling_distribution': self._sampling_distribution.to_state_dict(), + 'transition_accumulator': self._transition_accumulator.to_state_dict(), + } + + def from_state_dict(self, state_dict: dict[str, Any]) -> None: + """Deserialize and mutate replay buffer using state dictionary.""" + self.add_count = state_dict['add_count'] + self._transition_accumulator.from_state_dict( + state_dict['transition_accumulator'] + ) + self._sampling_distribution.from_state_dict( + state_dict['sampling_distribution'] + ) + + # Restore memory + memory_keys = state_dict['memory']['keys'] + # Each element of the list is a flattened replay element, unflatten them + # i.e., we have storage like: + # [[state, action, reward, next_state, is_terminal, episode_end], ...] + # and after unflattening we'll have: + # [ReplayElementT(...), ...] + memory_treedef: jax.tree_util.PyTreeDef = pickle.loads( + state_dict['memory']['treedef'] + ) + memory_values = map( + memory_treedef.unflatten, state_dict['memory']['values'] + ) + + # Create our new ordered dictionary from the restored keys and values + self._memory = collections.OrderedDict[ReplayItemID, ReplayElementT]( + zip(memory_keys, memory_values, strict=True) + ) + + @functools.lru_cache + def _make_checkpoint_manager( + self, checkpoint_dir: str + ) -> orbax.CheckpointManager: + """Create orbax checkpoint manager, cache the manager based on path.""" + return orbax.CheckpointManager( + checkpoint_dir, + checkpointers={ + 'replay': orbax.Checkpointer( + checkpointers.CheckpointHandler[ReplayBuffer](), + ) + }, + options=orbax.CheckpointManagerOptions( + max_to_keep=self._checkpoint_duration, + create=True, + ), + ) + + def save(self, checkpoint_dir: str, iteration_number: int): + """Save the ReplayBuffer attributes into a file. + + Args: + checkpoint_dir: the directory where numpy checkpoint files should be + saved. Must already exist. + iteration_number: iteration_number to use as a suffix in naming. + """ + checkpoint_manager = self._make_checkpoint_manager(checkpoint_dir) + checkpoint_manager.save(iteration_number, {'replay': self}) + + def load(self, checkpoint_dir: str, iteration_number: int): + """Restores from a checkpoint. + + Args: + checkpoint_dir: the directory where to read the checkpoint. + iteration_number: iteration_number to use as a suffix in naming. + """ + checkpoint_manager = self._make_checkpoint_manager(checkpoint_dir) + # NOTE: Make sure not to pass in `items={'replay': self}` as this will + # create a deep copy and we want to mutate in-place. + # If we don't pass items then we get back a state dictionary + # that we can use to mutate in-place. + state_dict = checkpoint_manager.restore(iteration_number) + self.from_state_dict(state_dict['replay']) diff --git a/dopamine/jax/replay_memory/samplers.py b/dopamine/jax/replay_memory/samplers.py new file mode 100644 index 00000000..0951a294 --- /dev/null +++ b/dopamine/jax/replay_memory/samplers.py @@ -0,0 +1,319 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Sampling distributions.""" + +import dataclasses +import typing +from typing import Any, Protocol + +from absl import logging +from dopamine.jax import checkpointers +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import sum_tree +import gin +import numpy as np +import numpy.typing as npt + +ReplayItemID = elements.ReplayItemID + + +@dataclasses.dataclass(frozen=True, kw_only=True) +class SampleMetadata: + keys: npt.NDArray[ReplayItemID] + + +@typing.runtime_checkable +class SamplingDistribution(checkpointers.Checkpointable, Protocol): + """A sampling distribution.""" + + def add(self, key: ReplayItemID, **kwargs: Any) -> None: + ... + + @typing.overload + def update(self, keys: npt.NDArray[ReplayItemID], **kwargs: Any) -> None: + ... + + @typing.overload + def update(self, key: ReplayItemID, **kwargs: Any) -> None: + ... + + def update( + self, keys: npt.NDArray[ReplayItemID] | ReplayItemID, **kwargs: Any + ) -> None: + ... + + def remove(self, key: ReplayItemID) -> None: + ... + + def sample(self, size: int) -> SampleMetadata: + ... + + def clear(self) -> None: + ... + + +class UniformSamplingDistribution(SamplingDistribution): + """A uniform sampling distribution.""" + + def __init__( + self, seed: np.random.Generator | np.random.SeedSequence | int | None + ) -> None: + # RNG generator + self._rng = np.random.default_rng(seed) + + # The following two datastructures are used for efficient sampling from + # our memory collection. It's not possible with Python only to efficiently + # sample from a dictionary without incurring an O(n) cost to materialize + # the keys / values. + # To efficiently sample from the dictionary we maintain two datastructures: + # 1) A mapping (as a dict) from dict keys -> contiguous local indices + # 2) A mapping (as a list) from local indices -> dict keys + # + # Now to sample from our dictionary we can sample from the range of + # local indices (of which we know the bounds for). Then we can fetch + # the dictionary keys associated with those indices to then lookup in our + # dictionary. The cost is now amortized constant across both operations. + # + # See `replay_buffer_test.py:testKeyMappingsForSampling` for an example. + + # A mapping (as a dict) from dict keys -> local indices + # e.g., consider a replay buffer with max size 100, the global key 100 would + # map to a local index of 0 (wrap around). + self._index_by_key = {} + # A reverse mapping (as an array) from local indices -> global keys + self._key_by_index = [] + + @property + def size(self) -> int: + return len(self._key_by_index) + + def add(self, key: ReplayItemID, **kwargs: Any) -> None: + if kwargs: + logging.log_first_n( + logging.WARN, + 'Got kwargs %r to `UniformDistribution.add`, ignoring...', + 1, + kwargs, + ) + # Keep track of the index of this key + self._index_by_key[key] = len(self._key_by_index) + self._key_by_index.append(key) + + @typing.overload + def update( + self, keys: npt.NDArray[ReplayItemID], *args: Any, **kwargs: Any + ) -> None: + ... + + @typing.overload + def update(self, key: ReplayItemID, *args: Any, **kwargs: Any) -> None: + ... + + def update( + self, + keys: npt.NDArray[ReplayItemID] | ReplayItemID, + *args: Any, + **kwargs: Any, + ) -> None: + logging.log_first_n( + logging.WARN, '`UniformDistribution.update` is a no-op, ignoring...', 1 + ) + + def remove(self, key: ReplayItemID) -> None: + if key not in self._index_by_key: + raise ValueError(f'Key {key} not found.') + # We now must resolve the index from this key to update our map + index = self._index_by_key[key] + # Swap the values of the oldest key and the latest key + # so we can perform efficient O(1) pop on the keys + self._key_by_index[index], self._key_by_index[-1] = ( + self._key_by_index[-1], + self._key_by_index[index], + ) + # Make sure we update the key->index mapping with the swapped index + self._index_by_key[self._key_by_index[index]] = index + # Pop the oldest key to get the index then pop the index from the dict + self._index_by_key.pop(self._key_by_index.pop()) + + def sample(self, size: int) -> SampleMetadata: + if not self._key_by_index: + raise ValueError('No keys to sample from.') + if size <= 0: + raise ValueError('Sample size must be positive.') + indices = self._rng.integers(len(self._key_by_index), size=size) + return SampleMetadata( + keys=np.fromiter( + (self._key_by_index[index] for index in indices), + dtype=np.int32, + count=size, + ) + ) + + def clear(self) -> None: + self._index_by_key.clear() + self._key_by_index.clear() + + def to_state_dict(self) -> dict[str, Any]: + return { + 'key_by_index': self._key_by_index, + 'index_by_key': self._index_by_key, + 'rng_state': self._rng.bit_generator.state, + } + + def from_state_dict(self, state_dict: dict[str, Any]) -> None: + self._key_by_index = state_dict['key_by_index'] + self._index_by_key = state_dict['index_by_key'] + + # Restore rng state + self._rng.bit_generator.state = state_dict['rng_state'] + + +@dataclasses.dataclass(frozen=True, kw_only=True) +class PrioritizedSampleMetadata(SampleMetadata): + probabilities: npt.NDArray[np.float64] + + +@gin.configurable +class PrioritizedSamplingDistribution(UniformSamplingDistribution): + """A prioritized sampling distribution.""" + + def __init__( + self, + seed: np.random.SeedSequence | int | None, + *, + priority_exponent: float = 1.0, + max_capacity: int, + ) -> None: + self._rng = np.random.default_rng(seed) + self._max_capacity = max_capacity + self._priority_exponent = priority_exponent + self._sum_tree = sum_tree.SumTree(self._max_capacity) + + super().__init__(self._rng) + + def add(self, key: ReplayItemID, *, priority: float) -> None: + super().add(key) + if priority is None: + priority = 0.0 + self._sum_tree.set( + self._index_by_key[key], + 0.0 if priority == 0.0 else priority**self._priority_exponent, + ) + + @typing.overload + def update( + self, + keys: npt.NDArray[ReplayItemID], + *, + priorities: npt.NDArray[np.float64], + ) -> None: + ... + + @typing.overload + def update(self, keys: ReplayItemID, *, priorities: float) -> None: + ... + + def update( + self, + keys: npt.NDArray[ReplayItemID] | ReplayItemID, + *, + priorities: npt.NDArray[np.float64] | float, + ) -> None: + if not isinstance(keys, np.ndarray): + keys = np.asarray([keys], dtype=np.int32) + + priorities = np.where( + priorities == 0.0, 0.0, priorities**self._priority_exponent + ) + self._sum_tree.set( + np.fromiter((self._index_by_key[key] for key in keys), dtype=np.int32), + priorities, + ) + + def remove(self, key: ReplayItemID) -> None: + index = self._index_by_key[key] + last_index = len(self._key_by_index) - 1 + if index == last_index: + # If index and last_index are the same, simply set the priority to 0.0. + self._sum_tree.set(index, 0.0) + else: + # Otherwise, swap priorities with current index and last index + # as that's how we pop the key from our datastructure. + # This will run in O(logn) where n is the # of elements in the tree + self._sum_tree.set( + np.asarray([index, last_index], dtype=np.int32), + np.asarray([self._sum_tree.get(last_index), 0.0]), + ) + super().remove(key) + + def sample(self, size: int) -> PrioritizedSampleMetadata: + if self._sum_tree.root == 0.0: + keys = super().sample(size).keys + return PrioritizedSampleMetadata( + keys=keys, + probabilities=np.full_like(keys, 1.0 / self.size, dtype=np.float64), + ) + + targets = self._rng.uniform(0.0, self._sum_tree.root, size=size) + indices = self._sum_tree.query(targets) + return PrioritizedSampleMetadata( + keys=np.fromiter( + (self._key_by_index[index] for index in indices), + count=size, + dtype=np.int32, + ), + probabilities=self._sum_tree.get(indices) / self._sum_tree.root, + ) + + def clear(self) -> None: + self._sum_tree.clear() + super().clear() + + def to_state_dict(self) -> dict[str, Any]: + return { + 'sum_tree': self._sum_tree.to_state_dict(), + **super().to_state_dict(), + } + + def from_state_dict(self, state_dict: dict[str, Any]): + super().from_state_dict(state_dict) + self._sum_tree.from_state_dict(state_dict['sum_tree']) + + +class SequentialSamplingDistribution(UniformSamplingDistribution): + """A sequential sampling distribution.""" + + def __init__( + self, + seed: np.random.Generator | np.random.SeedSequence | int, + sort_samples: bool = True, + ): + super().__init__(seed) + self._sort_samples = sort_samples + + def sample(self, size: int) -> SampleMetadata: + if not self._key_by_index: + raise ValueError('No keys to sample from.') + if size <= 0: + raise ValueError('Sample size must be positive.') + if self._sort_samples: + self._key_by_index.sort() + return SampleMetadata( + keys=np.fromiter( + self._key_by_index, + dtype=np.int32, + count=size, + ) + ) diff --git a/dopamine/jax/replay_memory/sum_tree.py b/dopamine/jax/replay_memory/sum_tree.py new file mode 100644 index 00000000..649682fb --- /dev/null +++ b/dopamine/jax/replay_memory/sum_tree.py @@ -0,0 +1,163 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Sum Tree.""" + +import typing +from typing import Any + +from dopamine.jax import checkpointers +import numpy as np +import numpy.typing as npt + + +class SumTree(checkpointers.Checkpointable): + """A vectorized sum tree in numpy.""" + + def __init__(self, capacity: int) -> None: + assert capacity > 0, 'Capacity to sum tree must be positive.' + self._capacity = capacity + self._depth = int(np.ceil(np.log2(capacity))) + 1 + + self._first_leaf_offset = (2 ** (self._depth - 1)) - 1 + self._nodes = np.zeros((2**self._depth) - 1, dtype=np.float64) + self.max_recorded_priority = 1.0 + + @typing.overload + def set(self, index: int, value: float) -> None: + ... + + @typing.overload + def set( + self, index: npt.NDArray[np.int_], value: npt.NDArray[np.float64] + ) -> None: + ... + + def set( + self, + indices: npt.NDArray[np.int_] | int, + values: npt.NDArray[np.float64] | float, + ) -> None: + """Set the value at a given leaf node index.""" + if isinstance(indices, (int, np.integer)): + indices = np.asarray([indices], np.int32) + if isinstance(values, (int, float, np.floating)): + values = np.asarray([values], np.float64) + assert ( + indices.shape == values.shape + ), 'Indices and values must have the same shape.' + assert (values >= 0.0).all(), 'Values must be positive.' + self.max_recorded_priority = max(self.max_recorded_priority, max(values)) + node_indices = self._first_leaf_offset + indices + delta_values = values - self._nodes[node_indices] + + # De-duplicate indices, otherwise this can result in delta_values being + # applied multiple times to the same index, which results in the incorrect + # priorities being set. + node_indices, unique_idx = np.unique(node_indices, return_index=True) + delta_values = delta_values[unique_idx] + for _ in reversed(range(self._depth - 1)): + # Use np.add.at which will accumulate duplicate indices + np.add.at(self._nodes, node_indices, delta_values) + node_indices = (node_indices - 1) // 2 + + assert ( + node_indices == 0 + ).all(), f'Sum tree traversal failed with {node_indices}.' + np.add.at(self._nodes, node_indices, delta_values) + + @typing.overload + def get(self, index: int) -> float: + ... + + @typing.overload + def get(self, index: npt.NDArray[np.int_]) -> npt.NDArray[np.float64]: + ... + + def get( + self, index: npt.NDArray[np.int_] | int + ) -> npt.NDArray[np.float64] | float: + """Get the value at a given leaf node index.""" + return self._nodes[self._first_leaf_offset + index] + + @property + def root(self) -> float: + """The root value (total sum) of the sum tree.""" + return self._nodes[0] + + @typing.overload + def query(self, target: float) -> int: + ... + + @typing.overload + def query(self, target: npt.NDArray[np.float64]) -> npt.NDArray[np.int_]: + ... + + def query( + self, targets: npt.NDArray[np.float64] | float + ) -> npt.NDArray[np.int_] | int: + """Find the smallest index where target < cumulative value up to index. + + This functions like the CDF for a multi-nomial distribution allowing us + to sample from this distribution through inverse CDF sampling. + + Args: + targets: a numpy array or float of query values. Targets must be in the + range [0, `root`] where `root` is the root value of the sum tree. + + Returns: + either a numpy array of indices or a single index depending on the query. + """ + if isinstance(targets, (int, float)): + targets = np.asarray([targets], np.float64) + if not ((targets >= 0) & (targets < self.root)).all(): + raise ValueError(f'Targets must be in the interval [0.0, {self.root}).') + + # We'll traverse the tree for all indices at once using masking + node_indices = np.zeros_like(targets, dtype=np.int32) + # While we're still traversing + while (traversal_mask := node_indices < self._first_leaf_offset).any(): + # Our intermediate targets should always be less than intermediate nodes + assert (targets < self._nodes[node_indices]).all() + left_node_indices = 2 * node_indices + 1 + left_node_sums = self._nodes[left_node_indices] + right_node_indices = left_node_indices + 1 + + # Traverse the tree but only if that index isn't masked + node_indices = np.where( + traversal_mask, + np.where( + targets < left_node_sums, + left_node_indices, + right_node_indices, + ), + node_indices, + ) + targets = np.where( + targets < left_node_sums, + targets, + targets - left_node_sums, + ) + + return node_indices - self._first_leaf_offset + + def clear(self) -> None: + self._nodes.fill(0.0) + + def to_state_dict(self) -> dict[str, Any]: + return {'nodes': self._nodes} + + def from_state_dict(self, state_dict: dict[str, Any]): + assert self._nodes.shape == state_dict['nodes'].shape + self._nodes = state_dict['nodes'] diff --git a/dopamine/jax/serialization.py b/dopamine/jax/serialization.py new file mode 100644 index 00000000..7d7194ae --- /dev/null +++ b/dopamine/jax/serialization.py @@ -0,0 +1,126 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""MessagePack serialization hooks. + +This is necesarry as Orbax doesn't support serializing Numpy structured arrays. +TensorStore (the underlying backend for Orbax) does support structured arrays. +If Orbax ever adds support for structured arrays, we can remove this altogether +and use Orbax to serialize things like the replay buffer without requiring +a custom checkpoint handler (jax/checkpointers.py). +""" + +import ast +import functools +import typing +from typing import Any, Callable, Optional, Tuple, Union + +import numpy as np +from typing_extensions import Literal, NotRequired, TypedDict # pytype: disable=not-supported-yet # pylint: disable=g-multiple-import + +Shape = Tuple[int, ...] + + +class NumpyEncoding(TypedDict, total=False): + """Numpy encoding dictionary.""" + + dtype: str + shape: Shape + data: bytes + order: NotRequired[Literal['C', 'F']] # pytype: disable=not-indexable + + +class LongIntegerEncoding(TypedDict): + """Encoding for ints longer than 32-bits which MessagePack doesn't support.""" + + integer: str + + +@functools.singledispatch +def encode(obj: Any, chain: Optional[Callable[[Any], Any]] = None) -> Any: + """Encode object. Encoders will register with `@encode.register`.""" + return obj if chain is None else chain(obj) + + +@encode.register(np.ndarray) +@encode.register(np.bool_) +@encode.register(np.number) +def _( + array: Union[np.ndarray, np.bool_, np.number], + chain: Optional[Callable[[Any], Any]] = None, +) -> NumpyEncoding: + """Encode numpy array.""" + del chain + + encoded: NumpyEncoding = {'shape': array.shape, 'data': array.tobytes()} + + # Encode the dtype using repr and parse it on decode using `ast.literal_eval` + if array.dtype.kind == 'V': + encoded['dtype'] = repr(array.dtype.descr) + else: + encoded['dtype'] = repr(array.dtype.str) + + # Store the order if we have one + if array.flags['C_CONTIGUOUS'] and not array.flags['F_CONTIGUOUS']: + encoded['order'] = 'C' # pytype: disable=annotation-type-mismatch + elif not array.flags['C_CONTIGUOUS'] and array.flags['F_CONTIGUOUS']: + encoded['order'] = 'F' # pytype: disable=annotation-type-mismatch + + return encoded # pytype: disable=bad-return-type + + +@encode.register +def _( + integer: int, + chain: Optional[Callable[[Any], Any]] = None, +) -> Union[int, LongIntegerEncoding]: + """Encode integers longer than 32 bit as a string.""" + del chain + + if integer.bit_length() > 32: + return {'integer': str(integer)} + + return integer + + +@typing.overload +def decode( + obj: NumpyEncoding, chain: Optional[Callable[[Any], Any]] = None +) -> np.ndarray: + ... + + +@typing.overload +def decode( + obj: LongIntegerEncoding, chain: Optional[Callable[[Any], Any]] = None +) -> int: + ... + + +def decode(obj: Any, chain: Optional[Callable[[Any], Any]] = None) -> Any: + """Decode encoded object types.""" + # Would be really nice if TypedDict supported isinstance + if isinstance(obj, dict) and ( + 'dtype' in obj and 'shape' in obj and 'data' in obj + ): + return np.ndarray( + shape=obj['shape'], + dtype=np.dtype(ast.literal_eval(obj['dtype'])), + buffer=obj['data'], + order=obj.get('order', None), + ) + elif isinstance(obj, dict) and 'integer' in obj: + return int(obj.get('integer')) + else: + return obj if chain is None else chain(obj) # pytype: disable=bad-return-type diff --git a/dopamine/labs/atari_100k/atari_100k_rainbow_agent.py b/dopamine/labs/atari_100k/atari_100k_rainbow_agent.py index 9421be02..60798851 100644 --- a/dopamine/labs/atari_100k/atari_100k_rainbow_agent.py +++ b/dopamine/labs/atari_100k/atari_100k_rainbow_agent.py @@ -20,6 +20,8 @@ from absl import logging from dopamine.jax import networks from dopamine.jax.agents.full_rainbow import full_rainbow_agent +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import samplers import gin import jax import jax.numpy as jnp @@ -214,8 +216,9 @@ def _training_step_update(self): ) if self._replay_scheme == 'prioritized': - self._replay.set_priority( - self.replay_elements['indices'], jnp.sqrt(loss + 1e-10) + self._replay.update( + self.replay_elements['indices'], + priorities=jnp.sqrt(loss + 1e-10), ) if self.summary_writer is not None: @@ -350,21 +353,26 @@ def _store_transition( ): """Stores a transition when in training mode.""" is_prioritized = hasattr(self._replay, 'sum_tree') + # pylint: disable=protected-access + is_prioritized = isinstance( + self._replay._sampling_distribution, + samplers.PrioritizedSamplingDistribution, + ) if is_prioritized and priority is None: priority = onp.ones_like(reward) if self._replay_scheme == 'prioritized': - priority *= self._replay.sum_tree.max_recorded_priority - - to_store = (last_observation, action, reward, is_terminal, *args) - to_store = (onp.asarray(x) for x in to_store) - if not hasattr(self._replay, '_n_envs'): - to_store = (onp.squeeze(x) for x in to_store) - priority = onp.squeeze(priority) - elif hasattr(self._replay, '_n_envs') and not reward.shape: - to_store = ( - onp.expand_dims(x, 0) if not (x.shape and x.shape[0] == 1) else x - for x in to_store - ) - priority = onp.expand_dims(priority, 0) - if not self.eval_mode: - self._replay.add(*to_store, priority=priority, episode_end=episode_end) + priority *= ( + self._replay._sampling_distribution._sum_tree.max_recorded_priority + ) + + self._replay.add( + elements.TransitionElement( + last_observation, + action, + reward, + is_terminal, + episode_end, + ), + priority=priority, + *args, + ) diff --git a/dopamine/labs/atari_100k/configs/DER.gin b/dopamine/labs/atari_100k/configs/DER.gin index d8036c42..d152c909 100644 --- a/dopamine/labs/atari_100k/configs/DER.gin +++ b/dopamine/labs/atari_100k/configs/DER.gin @@ -4,18 +4,18 @@ import dopamine.jax.agents.full_rainbow.full_rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer import dopamine.labs.atari_100k.atari_100k_rainbow_agent +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxDQNAgent.gamma = 0.99 # Use 10 instead of 20 as done by SPR paper JaxDQNAgent.update_horizon = 10 # DER (instead of 3) -JaxDQNAgent.min_replay_history = 1600 # DER (instead of 20000) +JaxDQNAgent.min_replay_history = 1_600 # DER (instead of 20000) JaxDQNAgent.update_period = 1 # DER: Update every 1 step (rather than 4) -JaxDQNAgent.target_update_period = 2000 # DER: Target every 2000 updates +JaxDQNAgent.target_update_period = 2_000 # DER: Target every 2000 updates JaxDQNAgent.epsilon_train = 0.01 JaxDQNAgent.epsilon_eval = 0.001 -JaxDQNAgent.epsilon_decay_period = 2000 # agent steps +JaxDQNAgent.epsilon_decay_period = 2_000 # agent steps JaxDQNAgent.optimizer = 'adam' JaxFullRainbowAgent.noisy = True @@ -37,9 +37,13 @@ atari_lib.create_atari_environment.game_name = 'Pong' atari_lib.create_atari_environment.sticky_actions = False AtariPreprocessing.terminal_on_life_loss = True Runner.num_iterations = 10 -Runner.training_steps = 10000 # agent steps +Runner.training_steps = 10_000 # agent steps MaxEpisodeEvalRunner.num_eval_episodes = 100 # agent episodes -Runner.max_steps_per_episode = 27000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 # 1M as a proxy for unbounded memory +OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1_000_000 # 1M as a proxy for unbounded memory OutOfGraphPrioritizedReplayBuffer.batch_size = 32 + +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/labs/atari_100k/configs/DrQ.gin b/dopamine/labs/atari_100k/configs/DrQ.gin index fd1d8d41..142c77de 100644 --- a/dopamine/labs/atari_100k/configs/DrQ.gin +++ b/dopamine/labs/atari_100k/configs/DrQ.gin @@ -4,18 +4,18 @@ import dopamine.jax.agents.full_rainbow.full_rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer import dopamine.labs.atari_100k.atari_100k_rainbow_agent +import dopamine.tf.replay_memory.prioritized_replay_buffer # Parameters specific to DrQ are higlighted by comments JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 10 # DrQ (instead of 3) -JaxDQNAgent.min_replay_history = 1600 # DrQ (instead of 20000) +JaxDQNAgent.min_replay_history = 1_600 # DrQ (instead of 20000) JaxDQNAgent.update_period = 1 # DrQ (rather than 4) JaxDQNAgent.target_update_period = 1 # DrQ (rather than 8000) JaxDQNAgent.epsilon_train = 0.1 # DrQ (rather than 0.01) JaxDQNAgent.epsilon_eval = 0.05 # DrQ (rather than 0.001) -JaxDQNAgent.epsilon_decay_period = 5000 # DrQ +JaxDQNAgent.epsilon_decay_period = 5_000 # DrQ JaxDQNAgent.optimizer = 'adam' JaxFullRainbowAgent.noisy = False # DrQ (Efficient DQN) @@ -37,9 +37,13 @@ atari_lib.create_atari_environment.game_name = 'Pong' atari_lib.create_atari_environment.sticky_actions = False AtariPreprocessing.terminal_on_life_loss = True Runner.num_iterations = 10 -Runner.training_steps = 10000 # agent steps +Runner.training_steps = 10_000 # agent steps MaxEpisodeEvalRunner.num_eval_episodes = 100 # agent episodes -Runner.max_steps_per_episode = 27000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 # 1M as a proxy for unbounded memory +OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1_000_000 # 1M as a proxy for unbounded memory OutOfGraphPrioritizedReplayBuffer.batch_size = 32 + +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/labs/atari_100k/configs/DrQ_eps.gin b/dopamine/labs/atari_100k/configs/DrQ_eps.gin index 8926e525..cab65b47 100644 --- a/dopamine/labs/atari_100k/configs/DrQ_eps.gin +++ b/dopamine/labs/atari_100k/configs/DrQ_eps.gin @@ -4,19 +4,19 @@ import dopamine.jax.agents.full_rainbow.full_rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer import dopamine.labs.atari_100k.atari_100k_rainbow_agent +import dopamine.tf.replay_memory.prioritized_replay_buffer # Parameters specific to DrQ are higlighted by comments JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 10 # DrQ (instead of 3) -JaxDQNAgent.min_replay_history = 1600 # DrQ (instead of 20000) +JaxDQNAgent.min_replay_history = 1_600 # DrQ (instead of 20000) JaxDQNAgent.update_period = 1 # DrQ (rather than 4) JaxDQNAgent.target_update_period = 1 # DrQ (rather than 8000) # See "https://arxiv.org/abs/2108.13264" for more details. JaxDQNAgent.epsilon_train = 0.01 # DrQ(ε) JaxDQNAgent.epsilon_eval = 0.001 # DrQ(ε) -JaxDQNAgent.epsilon_decay_period = 5000 # DrQ +JaxDQNAgent.epsilon_decay_period = 5_000 # DrQ JaxDQNAgent.optimizer = 'adam' JaxFullRainbowAgent.noisy = False # DrQ (Efficient DQN) @@ -38,9 +38,13 @@ atari_lib.create_atari_environment.game_name = 'Pong' atari_lib.create_atari_environment.sticky_actions = False AtariPreprocessing.terminal_on_life_loss = True Runner.num_iterations = 10 -Runner.training_steps = 10000 # agent steps +Runner.training_steps = 10_000 # agent steps MaxEpisodeEvalRunner.num_eval_episodes = 100 # agent episodes -Runner.max_steps_per_episode = 27000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 # 1M as a proxy for unbounded memory +OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1_000_000 # 1M as a proxy for unbounded memory OutOfGraphPrioritizedReplayBuffer.batch_size = 32 + +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/labs/atari_100k/configs/OTRainbow.gin b/dopamine/labs/atari_100k/configs/OTRainbow.gin index d65dd899..4b5b5580 100644 --- a/dopamine/labs/atari_100k/configs/OTRainbow.gin +++ b/dopamine/labs/atari_100k/configs/OTRainbow.gin @@ -4,18 +4,18 @@ import dopamine.jax.agents.full_rainbow.full_rainbow_agent import dopamine.jax.networks import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer import dopamine.labs.atari_100k.atari_100k_rainbow_agent +import dopamine.tf.replay_memory.prioritized_replay_buffer # Parameters specific to OTRainbow are higlighted by comments JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 3 -JaxDQNAgent.min_replay_history = 20000 +JaxDQNAgent.min_replay_history = 20_000 JaxDQNAgent.update_period = 1 # OTRainbow: Update every 1 step (rather than 4) JaxDQNAgent.target_update_period = 500 # OTRainbow (instead of 8000) JaxDQNAgent.epsilon_train = 0.01 JaxDQNAgent.epsilon_eval = 0.001 -JaxDQNAgent.epsilon_decay_period = 50000 # OTRainbow (instead of 250000) +JaxDQNAgent.epsilon_decay_period = 50_000 # OTRainbow (instead of 250000) JaxDQNAgent.optimizer = 'adam' # Don't use noisy networks, dueling DQN, and double DQN. @@ -38,9 +38,13 @@ atari_lib.create_atari_environment.game_name = 'Pong' atari_lib.create_atari_environment.sticky_actions = False AtariPreprocessing.terminal_on_life_loss = True Runner.num_iterations = 10 -Runner.training_steps = 10000 # agent steps +Runner.training_steps = 10_000 # agent steps MaxEpisodeEvalRunner.num_eval_episodes = 100 # agent episodes -Runner.max_steps_per_episode = 27000 # agent steps +Runner.max_steps_per_episode = 27_000 # agent steps -OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1000000 # 1M as a proxy for unbounded memory +OutOfGraphPrioritizedReplayBuffer.replay_capacity = 1_000_000 # 1M as a proxy for unbounded memory OutOfGraphPrioritizedReplayBuffer.batch_size = 32 + +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 +PrioritizedSamplingDistribution.max_capacity = 1_000_000 diff --git a/dopamine/labs/atari_100k/configs/SPR.gin b/dopamine/labs/atari_100k/configs/SPR.gin index 7c0737b0..eacfec35 100644 --- a/dopamine/labs/atari_100k/configs/SPR.gin +++ b/dopamine/labs/atari_100k/configs/SPR.gin @@ -13,11 +13,11 @@ import dopamine.jax.networks import dopamine.discrete_domains.checkpointer import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer import dopamine.labs.atari_100k.replay_memory.subsequence_replay_buffer import dopamine.labs.atari_100k.spr_networks import dopamine.labs.atari_100k.spr_agent import dopamine.labs.atari_100k.atari_100k_rainbow_agent +import dopamine.tf.replay_memory.prioritized_replay_buffer # Parameters specific to DrQ are higlighted by comments JaxDQNAgent.gamma = 0.99 diff --git a/dopamine/labs/atari_100k/replay_memory/deterministic_sum_tree.py b/dopamine/labs/atari_100k/replay_memory/deterministic_sum_tree.py index f1e63243..0b63b332 100755 --- a/dopamine/labs/atari_100k/replay_memory/deterministic_sum_tree.py +++ b/dopamine/labs/atari_100k/replay_memory/deterministic_sum_tree.py @@ -16,7 +16,7 @@ import functools -from dopamine.replay_memory import sum_tree +from dopamine.tf.replay_memory import sum_tree import jax from jax import numpy as jnp import numpy as np diff --git a/dopamine/labs/cale/__init__.py b/dopamine/labs/cale/__init__.py new file mode 100644 index 00000000..a98a65d0 --- /dev/null +++ b/dopamine/labs/cale/__init__.py @@ -0,0 +1,14 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/dopamine/labs/cale/configs/ppo_cale.gin b/dopamine/labs/cale/configs/ppo_cale.gin new file mode 100644 index 00000000..a02a7f39 --- /dev/null +++ b/dopamine/labs/cale/configs/ppo_cale.gin @@ -0,0 +1,47 @@ +import dopamine.discrete_domains.atari_lib +import dopamine.continuous_domains.run_experiment +import dopamine.discrete_domains.gym_lib +import dopamine.jax.agents.ppo.ppo_agent +import dopamine.jax.agents.dqn.dqn_agent +import dopamine.labs.sac_from_pixels.continuous_networks +import dopamine.labs.cale.networks + +PPOAgent.network = @networks.PPOCALEConvNetwork +PPOAgent.update_period = 1024 # 8 * 128 +PPOAgent.optimizer = 'adam' +PPOAgent.max_gradient_norm = 0.5 +create_optimizer.learning_rate = 2.5e-4 +create_optimizer.eps = 1e-5 +create_optimizer.anneal_learning_rate = True +create_optimizer.anneal_steps = 117_600 # 980 iterations * 3 epochs * 10240 timesteps / 256 batches (TODO(psc): revisit this) +PPOAgent.num_epochs = 3 +PPOAgent.batch_size = 256 # 8 * 32 +PPOAgent.gamma = 0.99 +PPOAgent.lambda_ = 0.95 +PPOAgent.epsilon = 0.1 +PPOAgent.vf_coefficient = 0.5 +PPOAgent.entropy_coefficient = 0.01 +PPOAgent.clip_critic_loss = True +PPOAgent.seed = None # Seed with the current time +PPOAgent.stack_size = 4 + +PPOCALEAgent.epsilon_train = 0.01 +PPOCALEAgent.epsilon_eval = 0.001 +PPOCALEAgent.epsilon_decay_period = 250_000 +PPOCALEAgent.exploration_strategy = 'standard' + +PPOCALEConvNetwork.encoder_name = 'SAC' + +ContinuousRunner.create_environment_fn = @atari_lib.create_atari_environment +ContinuousTrainRunner.create_environment_fn = @atari_lib.create_atari_environment +create_atari_environment.game_name = 'Asterix' +#create_atari_environment.use_ppo_preprocessing = True +create_atari_environment.continuous_action_threshold = 0.5 +create_continuous_runner.schedule = 'continuous_train' +create_continuous_agent.agent_name = 'ppo_cale' +ContinuousRunner.num_iterations = 200 +ContinuousRunner.training_steps = 250_000 +ContinuousRunner.max_steps_per_episode = 27_000 + +ReplayBuffer.max_capacity = 1024 +ReplayBuffer.batch_size = 1024 diff --git a/dopamine/labs/cale/configs/ppo_cale_100k.gin b/dopamine/labs/cale/configs/ppo_cale_100k.gin new file mode 100644 index 00000000..a2f26631 --- /dev/null +++ b/dopamine/labs/cale/configs/ppo_cale_100k.gin @@ -0,0 +1,49 @@ +import dopamine.discrete_domains.atari_lib +import dopamine.continuous_domains.run_experiment +import dopamine.discrete_domains.gym_lib +import dopamine.jax.agents.ppo.ppo_agent +import dopamine.jax.agents.dqn.dqn_agent +import dopamine.labs.cale.networks + +PPOAgent.network = @networks.PPOCALEConvNetwork +PPOAgent.update_period = 1024 # 8 * 128 +PPOAgent.optimizer = 'adam' +PPOAgent.max_gradient_norm = 0.5 +create_optimizer.learning_rate = 2.5e-4 +create_optimizer.eps = 1e-5 +create_optimizer.anneal_learning_rate = False +#create_optimizer.anneal_steps = 117_600 # 980 iterations * 3 epochs * 10240 timesteps / 256 batches (TODO(psc): revisit this) +PPOAgent.num_epochs = 3 +PPOAgent.batch_size = 256 # 8 * 32 +PPOAgent.gamma = 0.99 +PPOAgent.lambda_ = 0.95 +PPOAgent.epsilon = 0.1 +PPOAgent.vf_coefficient = 0.5 +PPOAgent.entropy_coefficient = 0.01 +PPOAgent.clip_critic_loss = True +PPOAgent.seed = None # Seed with the current time +PPOAgent.stack_size = 4 + +PPOCALEAgent.epsilon_train = 0.1 +PPOCALEAgent.epsilon_eval = 0.05 +PPOCALEAgent.epsilon_decay_period = 5_000 +PPOCALEAgent.exploration_strategy = 'standard' + +PPOCALEConvNetwork.encoder_name = 'SAC' + +ContinuousRunner.create_environment_fn = @atari_lib.create_atari_environment +ContinuousTrainRunner.create_environment_fn = @atari_lib.create_atari_environment +create_atari_environment.game_name = 'Asterix' +#create_atari_environment.use_ppo_preprocessing = True +create_atari_environment.continuous_action_threshold = 0.5 +create_continuous_runner.schedule = 'continuous_train' +create_continuous_agent.agent_name = 'ppo_cale_100k' +ContinuousRunner.num_iterations = 10 +ContinuousRunner.training_steps = 10_000 +ContinuousRunner.max_steps_per_episode = 27_000 + +atari_lib.create_atari_environment.sticky_actions = False +AtariPreprocessing.terminal_on_life_loss = True + +ReplayBuffer.max_capacity = 1024 +ReplayBuffer.batch_size = 1024 diff --git a/dopamine/labs/cale/configs/sac_cale.gin b/dopamine/labs/cale/configs/sac_cale.gin new file mode 100644 index 00000000..57f615cc --- /dev/null +++ b/dopamine/labs/cale/configs/sac_cale.gin @@ -0,0 +1,45 @@ +import dopamine.discrete_domains.atari_lib +import dopamine.continuous_domains.run_experiment +import dopamine.discrete_domains.gym_lib +import dopamine.labs.cale.networks +import dopamine.jax.agents.sac.sac_agent +import dopamine.jax.agents.dqn.dqn_agent +import dopamine.labs.sac_from_pixels.continuous_networks + +SACAgent.reward_scale_factor = 0.1 +SACAgent.network = @networks.SACCALEConvNetwork +SACAgent.num_layers = 1 +SACAgent.hidden_units = 512 +SACAgent.gamma = 0.99 +SACAgent.update_horizon = 1 +SACAgent.min_replay_history = 20_000 # agent steps +SACAgent.update_period = 4 +SACAgent.target_update_type = 'soft' +SACAgent.target_smoothing_coefficient = 0.005 +SACAgent.target_entropy = None # Defaults to -num_action_dims/2 +SACAgent.optimizer = 'adam' +SACAgent.seed = None # Seed with the current time +SACAgent.observation_dtype = %sac_agent.IMAGE_DTYPE +SACAgent.stack_size = 4 +create_optimizer.learning_rate = 6.25e-5 +create_optimizer.eps = 1.5e-4 + +SACCALEAgent.epsilon_train = 0.01 +SACCALEAgent.epsilon_eval = 0.001 +SACCALEAgent.epsilon_decay_period = 250_000 +SACCALEAgent.exploration_strategy = 'standard' + +SACCALEConvNetwork.encoder_name = 'SAC' + +ContinuousRunner.create_environment_fn = @atari_lib.create_atari_environment +ContinuousTrainRunner.create_environment_fn = @atari_lib.create_atari_environment +create_atari_environment.game_name = 'Asterix' +create_atari_environment.continuous_action_threshold = 0.5 +create_continuous_runner.schedule = 'continuous_train' +create_continuous_agent.agent_name = 'sac_cale' +ContinuousRunner.num_iterations = 200 +ContinuousRunner.training_steps = 250_000 +ContinuousRunner.max_steps_per_episode = 27_000 + +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 diff --git a/dopamine/labs/cale/configs/sac_cale_100k.gin b/dopamine/labs/cale/configs/sac_cale_100k.gin new file mode 100644 index 00000000..0b758663 --- /dev/null +++ b/dopamine/labs/cale/configs/sac_cale_100k.gin @@ -0,0 +1,47 @@ +import dopamine.discrete_domains.atari_lib +import dopamine.continuous_domains.run_experiment +import dopamine.discrete_domains.gym_lib +import dopamine.jax.agents.sac.sac_agent +import dopamine.jax.agents.dqn.dqn_agent +import dopamine.labs.cale.networks + +SACAgent.reward_scale_factor = 0.1 +SACAgent.network = @networks.SACCALEConvNetwork +SACAgent.num_layers = 1 +SACAgent.hidden_units = 512 +SACAgent.gamma = 0.99 +SACAgent.update_horizon = 10 # DrQ (instead of 3) +SACAgent.min_replay_history = 1600 # DrQ (instead of 20000) +SACAgent.update_period = 1 # DrQ (rather than 4) +SACAgent.target_update_type = 'soft' +SACAgent.target_smoothing_coefficient = 0.005 +SACAgent.target_entropy = None # Defaults to -num_action_dims/2 +SACAgent.optimizer = 'adam' +SACAgent.seed = None # Seed with the current time +SACAgent.observation_dtype = %sac_agent.IMAGE_DTYPE +SACAgent.stack_size = 4 +create_optimizer.learning_rate = 0.0001 +create_optimizer.eps = 0.00015 + +SACCALEAgent.epsilon_train = 0.1 +SACCALEAgent.epsilon_eval = 0.05 +SACCALEAgent.epsilon_decay_period = 5_000 +SACCALEAgent.exploration_strategy = 'eps_greedy' + +SACCALEConvNetwork.encoder_name = 'SAC' + +ContinuousRunner.create_environment_fn = @atari_lib.create_atari_environment +ContinuousTrainRunner.create_environment_fn = @atari_lib.create_atari_environment +create_atari_environment.game_name = 'Asterix' +create_atari_environment.continuous_action_threshold = 0.5 +create_continuous_runner.schedule = 'continuous_train' +create_continuous_agent.agent_name = 'sac_cale_100k' +ContinuousRunner.num_iterations = 10 +ContinuousRunner.training_steps = 10_000 # agent steps +ContinuousRunner.max_steps_per_episode = 27_000 + +atari_lib.create_atari_environment.sticky_actions = False +AtariPreprocessing.terminal_on_life_loss = True + +ReplayBuffer.max_capacity = 1_000_000 +ReplayBuffer.batch_size = 32 diff --git a/dopamine/labs/cale/networks.py b/dopamine/labs/cale/networks.py new file mode 100644 index 00000000..7711b911 --- /dev/null +++ b/dopamine/labs/cale/networks.py @@ -0,0 +1,313 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Networks for running SAC/PPO on CALE.""" + +from typing import Callable, Optional, Tuple + +from absl import logging +from dopamine.jax import continuous_networks +from dopamine.jax import networks as discrete_networks +from dopamine.labs.sac_from_pixels import continuous_networks as pixel_continuous_networks +from flax import linen as nn +import gin +import jax +from jax import numpy as jnp + + +@gin.configurable +class NatureDQNEncoder(nn.Module): + """An encoder based on the original DQN network.""" + + projection_dimension: int = 512 + + @nn.compact + def __call__(self, x): + initializer = nn.initializers.xavier_uniform() + # Images are stored in the replay buffer as uint8. + x = x.astype(jnp.float32) / 255.0 + x = nn.Conv( + features=32, kernel_size=(8, 8), strides=(4, 4), kernel_init=initializer + )(x) + x = nn.relu(x) + x = nn.Conv( + features=64, kernel_size=(4, 4), strides=(2, 2), kernel_init=initializer + )(x) + x = nn.relu(x) + x = nn.Conv( + features=64, kernel_size=(3, 3), strides=(1, 1), kernel_init=initializer + )(x) + x = nn.relu(x) + x = jnp.reshape(x, -1) # Flatten + + critic_z = nn.Dense( + features=self.projection_dimension, kernel_init=initializer + )(x) + critic_z = nn.LayerNorm()(critic_z) + critic_z = nn.relu(critic_z) + + # Only the critic should train the convolution layers, so stop the + # gradients from the actor. + actor_z = nn.Dense( + features=self.projection_dimension, kernel_init=initializer + )(jax.lax.stop_gradient(x)) + actor_z = nn.relu(actor_z) + + return pixel_continuous_networks.SACEncoderOutputs(critic_z, actor_z) + + +@gin.configurable +class SACImpalaEncoder(nn.Module): + """An encoder based on the Impala encoder.""" + + projection_dimension: int = 512 + nn_scale: int = 1 + stack_sizes: Tuple[int, ...] = (16, 32, 32) + num_blocks: int = 2 + + def setup(self): + logging.info('\t Creating %s ...', self.__class__.__name__) + logging.info('\t projection_dimension: %s', self.projection_dimension) + logging.info('\t num_blocks: %s', self.num_blocks) + logging.info('\t nn_scale: %s', self.nn_scale) + logging.info('\t stack_sizes: %s', self.stack_sizes) + self._stacks = [ + discrete_networks.Stack( + num_ch=stack_size * self.nn_scale, num_blocks=self.num_blocks + ) + for stack_size in self.stack_sizes + ] + + @nn.compact + def __call__(self, x): + initializer = nn.initializers.xavier_uniform() + for stack in self._stacks: + x = stack(x) + + critic_z = nn.Dense( + features=self.projection_dimension, kernel_init=initializer + )(x) + critic_z = nn.LayerNorm()(critic_z) + critic_z = nn.relu(critic_z) + + # Only the critic should train the convolution layers, so stop the + # gradients from the actor. + actor_z = nn.Dense( + features=self.projection_dimension, kernel_init=initializer + )(jax.lax.stop_gradient(x)) + actor_z = nn.relu(actor_z) + + return pixel_continuous_networks.SACEncoderOutputs(critic_z, actor_z) + + +@gin.configurable +class SACCALEConvNetwork(nn.Module): + """A convolutional value and policy networks for SAC/PPO.""" + + action_shape: Tuple[int, ...] + num_layers: int = 2 + hidden_units: int = 256 + action_limits: Optional[Tuple[Tuple[float, ...], Tuple[float, ...]]] = None + encoder_name: str = 'NatureDQN' + sac_cls: nn.Module = continuous_networks.ActorCriticNetwork + + def setup(self): + logging.info( + '\t Creating %s with encoder %s...', + self.__class__.__name__, + self.encoder_name, + ) + if self.encoder_name == 'NatureDQN': + self._encoder = NatureDQNEncoder() + elif self.encoder_name == 'Impala': + self._encoder = SACImpalaEncoder() + elif self.encoder_name == 'SAC': + self._encoder = pixel_continuous_networks.SACEncoderNetwork() + else: + raise ValueError(f'Unrecognized encoder: {self.encoder_name}') + + self._sac_network = self.sac_cls( + self.action_shape, + self.num_layers, + self.hidden_units, + self.action_limits, + ) + + def __call__( + self, state: jnp.ndarray, key: jnp.ndarray, mean_action: bool = True + ) -> continuous_networks.ActorCriticOutput: + """Calls the SAC actor/critic networks. + + This has two important purposes: + 1. It is used to initialize all parameters of both networks. + 2. It is used to efficiently calculate the outputs of both the actor + and critic networks on a single input. + + Args: + state: An input state. + key: A PRNGKey to use to sample an action from the actor's output + distribution. + mean_action: If True, it will use the actor's mean action to feed to the + value network. Otherwise, it will use the sampled action. + + Returns: + A named tuple containing the outputs from both networks. + """ + encoding = self._encoder(state) + + actor_output = self._sac_network.actor(encoding.actor_z, key) + action = ( + actor_output.mean_action if mean_action else actor_output.sampled_action + ) + critic_output = self._sac_network.critic(encoding.critic_z, action) + + return continuous_networks.ActorCriticOutput(actor_output, critic_output) + + def actor( + self, state: jnp.ndarray, key: jnp.ndarray + ) -> continuous_networks.ActorOutput: + """Calls the SAC actor network. + + This can be called using network_def.apply(..., method=network_def.actor). + + Args: + state: An input state. + key: A PRNGKey to use to sample an action from the actor's output + distribution. + + Returns: + A named tuple containing a sampled action, the mean action, and the + likelihood of the sampled action. + """ + encoding = self._encoder(state) + return self._sac_network.actor(encoding.actor_z, key) + + def critic( + self, state: jnp.ndarray, action: jnp.ndarray + ) -> continuous_networks.CriticOutput: + """Calls the SAC critic network. + + SAC uses two Q networks to reduce overestimation bias. + This can be called using network_def.apply(..., method=network_def.critic). + + Args: + state: An input state. + action: An action to compute the value of. + + Returns: + A named tuple containing the Q values of each network. + """ + encoding = self._encoder(state) + return self._sac_network.critic(encoding.critic_z, action) + + +@gin.configurable +class PPOCALEConvNetwork(nn.Module): + """Actor critic network for PPO. + + Attributes: + action_shape: shape of the action. + num_layers: number of layers in the networks. + hidden_units: number of hidden units in the networks. + activation: activation function to use in the networks. + """ + + action_shape: Tuple[int, ...] + num_layers: int = 2 + hidden_units: int = 64 + activation: Callable[[jax.Array], jax.Array] = nn.tanh + action_limits: Optional[Tuple[Tuple[float, ...], Tuple[float, ...]]] = None + encoder_name: str = 'NatureDQN' + ppo_cls: nn.Module = continuous_networks.PPOActorCriticNetwork + + def setup(self): + logging.info( + '\t Creating %s with encoder %s...', + self.__class__.__name__, + self.encoder_name, + ) + if self.encoder_name == 'NatureDQN': + self._encoder = NatureDQNEncoder() + elif self.encoder_name == 'Impala': + self._encoder = SACImpalaEncoder() + elif self.encoder_name == 'SAC': + self._encoder = pixel_continuous_networks.SACEncoderNetwork() + else: + raise ValueError(f'Unrecognized encoder: {self.encoder_name}') + self._ppo_network = self.ppo_cls( + action_shape=self.action_shape, + action_limits=self.action_limits, + num_layers=self.num_layers, + hidden_units=self.hidden_units, + ) + + def __call__( + self, state: jnp.ndarray, key: jnp.ndarray + ) -> continuous_networks.PPOActorCriticOutput: + """Calls the actor/critic networks. + + This has two important purposes: + 1. It is used to initialize all parameters of both networks. + 2. It is used to efficiently calculate the outputs of both the actor + and critic networks on a single input. + + Args: + state: An input state. + key: A PRNGKey to use to sample an action from the actor's output + distribution. + + Returns: + A named tuple containing the outputs from both networks. + """ + encoding = self._encoder(state) + actor_output = self._ppo_network.actor(encoding.actor_z, key) + critic_output = self._ppo_network.critic(encoding.critic_z) + return continuous_networks.PPOActorCriticOutput(actor_output, critic_output) + + def actor( + self, + state: jnp.ndarray, + key: Optional[jnp.ndarray] = None, + action: Optional[jnp.ndarray] = None, + ) -> continuous_networks.PPOActorOutput: + """Calls the actor network. + + This can be called using network_def.apply(..., method=network_def.actor). + + Args: + state: An input state. + key: A PRNGKey to use to sample an action from the actor's output + distribution. + action: An action to use to feed to the actor network. + + Returns: + A named tuple containing the provided action, the likelihood of the + provided action, and the entropy of the action. + """ + encoding = self._encoder(state) + return self._ppo_network.actor(encoding.actor_z, key=key, action=action) + + def critic(self, state: jnp.ndarray) -> continuous_networks.PPOCriticOutput: + """Calls the critic network. + + This can be called using network_def.apply(..., method=network_def.critic). + + Args: + state: An input state. + + Returns: + A named tuple containing the Q value of the network. + """ + encoding = self._encoder(state) + return self._ppo_network.critic(encoding.critic_z) diff --git a/dopamine/labs/cale/ppo_cale.py b/dopamine/labs/cale/ppo_cale.py new file mode 100644 index 00000000..d808a57b --- /dev/null +++ b/dopamine/labs/cale/ppo_cale.py @@ -0,0 +1,171 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PPO Agent for CALE.""" + +import functools + +from absl import logging +from dopamine.jax.agents.dqn import dqn_agent +from dopamine.jax.agents.ppo import ppo_agent +from dopamine.labs.cale import utils +import gin +import jax +import jax.numpy as jnp +import numpy as np +import tensorflow as tf + + +@functools.partial( + jax.jit, + static_argnames=[ + 'network_def', + 'epsilon_decay_period', + 'min_replay_history', + 'epsilon_eval', + 'epsilon_train', + 'action_shape', + ], +) +def select_action_eps_greedy( + network_def, + params, + state, + rng, + training_steps, + min_replay_history, + epsilon_eval, + epsilon_train, + epsilon_decay_period, + action_shape, + eval_mode=False, +): + """Epsilon-greedy action selection.""" + epsilon = jnp.where( + eval_mode, + epsilon_eval, + dqn_agent.linearly_decaying_epsilon( + epsilon_decay_period, + training_steps, + min_replay_history, + epsilon_train, + ), + ) + rng, rng1, rng2, rng3 = jax.random.split(rng, num=4) + p = jax.random.uniform(rng1) + return rng, jnp.where( + p <= epsilon, + jax.random.uniform(rng2, action_shape), + ppo_agent.select_action(network_def, params, state, rng3)[1], + ) + + +@gin.configurable +class PPOCALEAgent(ppo_agent.PPOAgent): + """PPO Agent for CALE.""" + + def __init__( + self, + action_shape, + action_limits, + observation_shape, + epsilon_train=0.01, + epsilon_eval=0.001, + epsilon_decay_period=250_000, + exploration_strategy='standard', + log_action_distributions=True, + summary_writer=None, + ): + """Initialize agent.""" + super().__init__( + action_shape=action_shape, + action_limits=action_limits, + observation_shape=observation_shape, + summary_writer=summary_writer, + ) + logging.info('\t epsilon_train: %f', epsilon_train) + logging.info('\t epsilon_eval: %f', epsilon_eval) + logging.info('\t epsilon_decay_period: %d', epsilon_decay_period) + logging.info('\t exploration_strategy: %s', exploration_strategy) + self.epsilon_train = epsilon_train + self.epsilon_eval = epsilon_eval + self.epsilon_decay_period = epsilon_decay_period + self.exploration_strategy = exploration_strategy + self.action_distributions = None + if log_action_distributions: + self.action_distributions = np.zeros((18,)) + + def _select_action(self): + self._rng, action = select_action_eps_greedy( + self.network_def, + self.network_params, + self.state, + self._rng, + self.training_steps, + self.min_replay_history, + self.epsilon_eval, + self.epsilon_train, + self.epsilon_decay_period, + self.action_shape, + eval_mode=self.eval_mode, + ) + self.action = np.asarray(action) + return self.action + + def _maybe_log_action_distribution(self): + if self.action_distributions is not None: + action_int = utils.get_action_number( + self.action[0], self.action[1], self.action[2] + ) + self.action_distributions[action_int] += 1 + + def begin_episode(self, observation): + if self.exploration_strategy == 'standard': + self.action = super().begin_episode(observation) + else: + self._reset_state() + self._record_observation(observation) + if not self.eval_mode: + self._train_step() + + self.action = self._select_action() + + self._maybe_log_action_distribution() + return self.action + + def step(self, reward, observation): + if self.exploration_strategy == 'standard': + self.action = super().step(reward, observation) + else: + self._last_observation = self._observation + self._record_observation(observation) + + if not self.eval_mode: + self._store_transition( + self._last_observation, self.action, reward, False + ) + self._train_step() + + self.action = self._select_action() + + self._maybe_log_action_distribution() + return self.action + + def bundle_and_checkpoint(self, checkpoint_dir, iteration_number): + if self.action_distributions is not None: + filename = f'{checkpoint_dir}/action_distributions_{iteration_number}.npy' + with tf.io.gfile.GFile(filename, 'w') as f: + np.save(f, self.action_distributions) + + return super().bundle_and_checkpoint(checkpoint_dir, iteration_number) diff --git a/dopamine/labs/cale/sac_cale.py b/dopamine/labs/cale/sac_cale.py new file mode 100644 index 00000000..1112880c --- /dev/null +++ b/dopamine/labs/cale/sac_cale.py @@ -0,0 +1,184 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAC Agent for CALE.""" + +import functools + +from absl import logging +from dopamine.jax.agents.dqn import dqn_agent +from dopamine.jax.agents.sac import sac_agent +from dopamine.labs.cale import utils +import gin +import jax +import jax.numpy as jnp +import numpy as np +import tensorflow as tf + + +@functools.partial( + jax.jit, + static_argnames=[ + 'network_def', + 'epsilon_decay_period', + 'min_replay_history', + 'epsilon_eval', + 'epsilon_train', + 'action_shape', + 'action_dtype', + ], +) +def select_action_eps_greedy( + network_def, + params, + state, + rng, + training_steps, + min_replay_history, + epsilon_eval, + epsilon_train, + epsilon_decay_period, + action_shape, + action_dtype, + action_limits, + eval_mode=False, +): + """Epsilon-greedy action selection.""" + epsilon = jnp.where( + eval_mode, + epsilon_eval, + dqn_agent.linearly_decaying_epsilon( + epsilon_decay_period, + training_steps, + min_replay_history, + epsilon_train, + ), + ) + rng, rng1, rng2, rng3 = jax.random.split(rng, num=4) + p = jax.random.uniform(rng1) + return rng, jnp.where( + p <= epsilon, + jax.random.uniform( + rng2, action_shape, action_dtype, action_limits[0], action_limits[1] + ), + sac_agent.select_action( + network_def, params, state, rng3, eval_mode=eval_mode + )[1], + ) + + +@gin.configurable +class SACCALEAgent(sac_agent.SACAgent): + """SAC Agent for CALE.""" + + def __init__( + self, + action_shape, + action_limits, + observation_shape, + action_dtype=jnp.float32, + observation_dtype=jnp.float32, + epsilon_train=0.01, + epsilon_eval=0.001, + epsilon_decay_period=250_000, + exploration_strategy='standard', + log_action_distributions=False, + summary_writer=None, + ): + """Initialize agent.""" + super().__init__( + action_shape=action_shape, + action_limits=action_limits, + observation_shape=observation_shape, + action_dtype=action_dtype, + observation_dtype=observation_dtype, + summary_writer=summary_writer, + ) + logging.info('\t epsilon_train: %f', epsilon_train) + logging.info('\t epsilon_eval: %f', epsilon_eval) + logging.info('\t epsilon_decay_period: %d', epsilon_decay_period) + logging.info('\t exploration_strategy: %s', exploration_strategy) + self.epsilon_train = epsilon_train + self.epsilon_eval = epsilon_eval + self.epsilon_decay_period = epsilon_decay_period + self.exploration_strategy = exploration_strategy + self.action_distributions = None + if log_action_distributions: + self.action_distributions = np.zeros((18,)) + + def _select_action(self): + self._rng, action = select_action_eps_greedy( + self.network_def, + self.network_params, + self.state, + self._rng, + self.training_steps, + self.min_replay_history, + self.epsilon_eval, + self.epsilon_train, + self.epsilon_decay_period, + self.action_shape, + self.action_dtype, + self.action_limits, + eval_mode=self.eval_mode, + ) + self.action = np.asarray(action) + return self.action + + def _maybe_log_action_distribution(self): + if self.action_distributions is not None: + action_int = utils.get_action_number( + self.action[0], self.action[1], self.action[2] + ) + self.action_distributions[action_int] += 1 + + def begin_episode(self, observation): + if self.exploration_strategy == 'standard': + self.action = super().begin_episode(observation) + else: + self._reset_state() + self._record_observation(observation) + if not self.eval_mode: + self._train_step() + + self.action = self._select_action() + + self._maybe_log_action_distribution() + return self.action + + def step(self, reward, observation): + if self.exploration_strategy == 'standard': + self.action = super().step(reward, observation) + else: + self._last_observation = self._observation + self._record_observation(observation) + + if not self.eval_mode: + self._store_transition( + self._last_observation, self.action, reward, False + ) + self._train_step() + + self.action = self._select_action() + + self._maybe_log_action_distribution() + return self.action + + def bundle_and_checkpoint(self, checkpoint_dir, iteration_number): + if self.action_distributions is not None: + filename = f'{checkpoint_dir}/action_distributions_{iteration_number}.npy' + with tf.io.gfile.GFile(filename, 'w') as f: + np.save(f, self.action_distributions) + + return super().bundle_and_checkpoint(checkpoint_dir, iteration_number) diff --git a/dopamine/labs/cale/utils.py b/dopamine/labs/cale/utils.py new file mode 100644 index 00000000..88f46f7e --- /dev/null +++ b/dopamine/labs/cale/utils.py @@ -0,0 +1,94 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Common utilities for CALE agents.""" + +import numpy as np + + +# Take from common/Constants.cpp +_ACTION_STR_TO_INT = { + 'PLAYER_A_NOOP': 0, + 'PLAYER_A_FIRE': 1, + 'PLAYER_A_UP': 2, + 'PLAYER_A_RIGHT': 3, + 'PLAYER_A_LEFT': 4, + 'PLAYER_A_DOWN': 5, + 'PLAYER_A_UPRIGHT': 6, + 'PLAYER_A_UPLEFT': 7, + 'PLAYER_A_DOWNRIGHT': 8, + 'PLAYER_A_DOWNLEFT': 9, + 'PLAYER_A_UPFIRE': 10, + 'PLAYER_A_RIGHTFIRE': 11, + 'PLAYER_A_LEFTFIRE': 12, + 'PLAYER_A_DOWNFIRE': 13, + 'PLAYER_A_UPRIGHTFIRE': 14, + 'PLAYER_A_UPLEFTFIRE': 15, + 'PLAYER_A_DOWNRIGHTFIRE': 16, + 'PLAYER_A_DOWNLEFTFIRE': 17, + 'PLAYER_B_NOOP': 18, + 'PLAYER_B_FIRE': 19, + 'PLAYER_B_UP': 20, + 'PLAYER_B_RIGHT': 21, + 'PLAYER_B_LEFT': 22, + 'PLAYER_B_DOWN': 23, + 'PLAYER_B_UPRIGHT': 24, + 'PLAYER_B_UPLEFT': 25, + 'PLAYER_B_DOWNRIGHT': 26, + 'PLAYER_B_DOWNLEFT': 27, + 'PLAYER_B_UPFIRE': 28, + 'PLAYER_B_RIGHTFIRE': 29, + 'PLAYER_B_LEFTFIRE': 30, + 'PLAYER_B_DOWNFIRE': 31, + 'PLAYER_B_UPRIGHTFIRE': 32, + 'PLAYER_B_UPLEFTFIRE': 33, + 'PLAYER_B_DOWNRIGHTFIRE': 34, + 'PLAYER_B_DOWNLEFTFIRE': 35, + '__invalid__36': 36, + '__invalid__37': 37, + '__invalid__38': 38, + '__invalid__39': 39, + 'RESET': 40, + 'UNDEFINED': 41, + 'RANDOM': 42, +} + + +def _polar_to_cartesian(r, theta): + return (r * np.cos(theta), r * np.sin(theta)) + + +def _polar_to_discrete_action(r, theta, fire, threshold=0.5): + """Convert actions from polar to discrete (strings).""" + x, y = _polar_to_cartesian(r, theta) + action = '' + if y > threshold: + action = 'UP' + elif y < -threshold: + action = 'DOWN' + if x > threshold: + action += 'RIGHT' + elif x < -threshold: + action += 'LEFT' + if fire > threshold: + action += 'FIRE' + if not action: + action = 'NOOP' + return 'PLAYER_A_' + action + + +def get_action_number(r, theta, fire, threshold=0.5): + return _ACTION_STR_TO_INT[ + _polar_to_discrete_action(r, theta, fire, threshold) + ] diff --git a/dopamine/labs/environments/brax/sac_brax.gin b/dopamine/labs/environments/brax/sac_brax.gin index 0003f5f2..ee0aa571 100644 --- a/dopamine/labs/environments/brax/sac_brax.gin +++ b/dopamine/labs/environments/brax/sac_brax.gin @@ -8,10 +8,10 @@ import dopamine.jax.agents.sac.sac_agent import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.continuous_networks import dopamine.labs.environments.brax.brax_lib -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer SACAgent.reward_scale_factor = 0.1 -SACAgent.network = @continuous_networks.SACNetwork +SACAgent.network = @continuous_networks.ActorCriticNetwork SACAgent.num_layers = 2 SACAgent.hidden_units = 256 SACAgent.gamma = 0.99 diff --git a/dopamine/labs/environments/minatar/dqn_asterix.gin b/dopamine/labs/environments/minatar/dqn_asterix.gin index 13c09c40..9de1004c 100644 --- a/dopamine/labs/environments/minatar/dqn_asterix.gin +++ b/dopamine/labs/environments/minatar/dqn_asterix.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %minatar_env.ASTERIX_SHAPE diff --git a/dopamine/labs/environments/minatar/dqn_breakout.gin b/dopamine/labs/environments/minatar/dqn_breakout.gin index c16af1b8..fbb0b014 100644 --- a/dopamine/labs/environments/minatar/dqn_breakout.gin +++ b/dopamine/labs/environments/minatar/dqn_breakout.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %minatar_env.BREAKOUT_SHAPE diff --git a/dopamine/labs/environments/minatar/dqn_freeway.gin b/dopamine/labs/environments/minatar/dqn_freeway.gin index b183c3be..73ff55fb 100644 --- a/dopamine/labs/environments/minatar/dqn_freeway.gin +++ b/dopamine/labs/environments/minatar/dqn_freeway.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %minatar_env.FREEWAY_SHAPE diff --git a/dopamine/labs/environments/minatar/dqn_seaquest.gin b/dopamine/labs/environments/minatar/dqn_seaquest.gin index b713c7fe..cd7e40c5 100644 --- a/dopamine/labs/environments/minatar/dqn_seaquest.gin +++ b/dopamine/labs/environments/minatar/dqn_seaquest.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %minatar_env.SEAQUEST_SHAPE diff --git a/dopamine/labs/environments/minatar/dqn_space_invaders.gin b/dopamine/labs/environments/minatar/dqn_space_invaders.gin index d4b3a515..ebfa9fe9 100644 --- a/dopamine/labs/environments/minatar/dqn_space_invaders.gin +++ b/dopamine/labs/environments/minatar/dqn_space_invaders.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer import flax JaxDQNAgent.observation_shape = %minatar_env.SPACE_INVADERS_SHAPE diff --git a/dopamine/labs/environments/minatar/quantile_asterix.gin b/dopamine/labs/environments/minatar/quantile_asterix.gin index 9af0f90e..04b4c7f3 100644 --- a/dopamine/labs/environments/minatar/quantile_asterix.gin +++ b/dopamine/labs/environments/minatar/quantile_asterix.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.quantile.quantile_agent import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxQuantileAgent.observation_shape = %minatar_env.ASTERIX_SHAPE JaxQuantileAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/quantile_breakout.gin b/dopamine/labs/environments/minatar/quantile_breakout.gin index 5cbd123a..c03ba49f 100644 --- a/dopamine/labs/environments/minatar/quantile_breakout.gin +++ b/dopamine/labs/environments/minatar/quantile_breakout.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.quantile.quantile_agent import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxQuantileAgent.observation_shape = %minatar_env.BREAKOUT_SHAPE JaxQuantileAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/quantile_freeway.gin b/dopamine/labs/environments/minatar/quantile_freeway.gin index 1aa1de2e..b2c2a8d1 100644 --- a/dopamine/labs/environments/minatar/quantile_freeway.gin +++ b/dopamine/labs/environments/minatar/quantile_freeway.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.quantile.quantile_agent import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxQuantileAgent.observation_shape = %minatar_env.FREEWAY_SHAPE JaxQuantileAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/quantile_seaquest.gin b/dopamine/labs/environments/minatar/quantile_seaquest.gin index b01f1d84..6445bf61 100644 --- a/dopamine/labs/environments/minatar/quantile_seaquest.gin +++ b/dopamine/labs/environments/minatar/quantile_seaquest.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.labs.environments.minatar.minatar_env import dopamine.jax.agents.quantile.quantile_agent -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxQuantileAgent.observation_shape = %minatar_env.SEAQUEST_SHAPE JaxQuantileAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/quantile_space_invaders.gin b/dopamine/labs/environments/minatar/quantile_space_invaders.gin index b44cb21c..63960a85 100644 --- a/dopamine/labs/environments/minatar/quantile_space_invaders.gin +++ b/dopamine/labs/environments/minatar/quantile_space_invaders.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.quantile.quantile_agent import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxQuantileAgent.observation_shape = %minatar_env.SPACE_INVADERS_SHAPE JaxQuantileAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/rainbow_asterix.gin b/dopamine/labs/environments/minatar/rainbow_asterix.gin index b600c594..8cfb7493 100644 --- a/dopamine/labs/environments/minatar/rainbow_asterix.gin +++ b/dopamine/labs/environments/minatar/rainbow_asterix.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %minatar_env.ASTERIX_SHAPE JaxRainbowAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/rainbow_breakout.gin b/dopamine/labs/environments/minatar/rainbow_breakout.gin index 22c0a209..5380f145 100644 --- a/dopamine/labs/environments/minatar/rainbow_breakout.gin +++ b/dopamine/labs/environments/minatar/rainbow_breakout.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %minatar_env.BREAKOUT_SHAPE JaxRainbowAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/rainbow_freeway.gin b/dopamine/labs/environments/minatar/rainbow_freeway.gin index 99d2d380..bf89d4ad 100644 --- a/dopamine/labs/environments/minatar/rainbow_freeway.gin +++ b/dopamine/labs/environments/minatar/rainbow_freeway.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %minatar_env.FREEWAY_SHAPE JaxRainbowAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/rainbow_seaquest.gin b/dopamine/labs/environments/minatar/rainbow_seaquest.gin index 08a0f23a..c4fcb54a 100644 --- a/dopamine/labs/environments/minatar/rainbow_seaquest.gin +++ b/dopamine/labs/environments/minatar/rainbow_seaquest.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %minatar_env.SEAQUEST_SHAPE JaxRainbowAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/environments/minatar/rainbow_space_invaders.gin b/dopamine/labs/environments/minatar/rainbow_space_invaders.gin index edbaa0de..e8ece6dc 100644 --- a/dopamine/labs/environments/minatar/rainbow_space_invaders.gin +++ b/dopamine/labs/environments/minatar/rainbow_space_invaders.gin @@ -5,7 +5,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.rainbow.rainbow_agent import dopamine.jax.networks import dopamine.labs.environments.minatar.minatar_env -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxRainbowAgent.observation_shape = %minatar_env.SPACE_INVADERS_SHAPE JaxRainbowAgent.observation_dtype = %minatar_env.DTYPE diff --git a/dopamine/labs/moes/architectures/networks.py b/dopamine/labs/moes/architectures/networks.py index 75e0bffe..68f3a26f 100644 --- a/dopamine/labs/moes/architectures/networks.py +++ b/dopamine/labs/moes/architectures/networks.py @@ -140,6 +140,7 @@ class MoEType(enum.Enum): BASELINE = 'BASELINE' # Won't use MoE modules. MOE = 'MOE' SOFTMOE = 'SOFTMOE' + EXPERTCHOICE = 'EXPERTCHOICE' SIMPLICIAL_EMBEDDING_V1 = 'SIMPLICIAL_EMBEDDING_V1' # Won't use MoE modules. SIMPLICIAL_EMBEDDING_V2 = 'SIMPLICIAL_EMBEDDING_V2' # Won't use MoE modules. @@ -441,9 +442,9 @@ def __call__(self, x: jax.Array, *, key: jax.Array) -> types.NetworkReturn: x = jnp.reshape(x, [x.shape[0] * x.shape[1], x.shape[2]]) moe_out = moe_net(x, key=key) x = moe_out.values + x_hidden = moe_out.experts_hidden x = x.reshape((-1)) # flatten - x_hidden = x if self.use_extra_linear_layer: x = nn.Dense(features=512, kernel_init=initializer)(x) x = nn.relu(x) @@ -624,8 +625,10 @@ def __call__(self, x, support, eval_mode=False, key=None): raise ValueError(f'Invalid routing type: {self.routing_type}') moe_out = moe_net(x, key=key_3) x = moe_out.values + x_hidden = moe_out.experts_hidden + x = x.reshape((-1)) # flatten - x_hidden = x + if self.use_extra_linear_layer: x = nn.Dense(features=512, kernel_init=initializer)(x) x = nn.relu(x) diff --git a/dopamine/labs/moes/configs/DER.gin b/dopamine/labs/moes/configs/DER.gin index 8689e7d4..5413a9c3 100644 --- a/dopamine/labs/moes/configs/DER.gin +++ b/dopamine/labs/moes/configs/DER.gin @@ -6,7 +6,7 @@ import dopamine.jax.agents.full_rainbow.full_rainbow_agent import dopamine.jax.networks import dopamine.labs.moes.architectures.networks import dopamine.labs.atari_100k.atari_100k_rainbow_agent -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxDQNAgent.gamma = 0.99 # Use 10 instead of 20 as done by SPR paper diff --git a/dopamine/labs/moes/configs/dqn.gin b/dopamine/labs/moes/configs/dqn.gin index 2f102f15..e1ad8a16 100644 --- a/dopamine/labs/moes/configs/dqn.gin +++ b/dopamine/labs/moes/configs/dqn.gin @@ -3,7 +3,7 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.labs.moes.architectures.networks -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 1 diff --git a/dopamine/labs/moes/configs/dqn_acrobot_moe.gin b/dopamine/labs/moes/configs/dqn_acrobot_moe.gin index 16a5cc7a..2f6918f3 100644 --- a/dopamine/labs/moes/configs/dqn_acrobot_moe.gin +++ b/dopamine/labs/moes/configs/dqn_acrobot_moe.gin @@ -3,7 +3,7 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.labs.moes.architectures.networks -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer JaxDQNAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE JaxDQNAgent.observation_dtype = %jax_networks.ACROBOT_OBSERVATION_DTYPE diff --git a/dopamine/labs/moes/configs/dqn_cartpole_moe.gin b/dopamine/labs/moes/configs/dqn_cartpole_moe.gin index 1f930533..855c51ed 100644 --- a/dopamine/labs/moes/configs/dqn_cartpole_moe.gin +++ b/dopamine/labs/moes/configs/dqn_cartpole_moe.gin @@ -3,7 +3,7 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.labs.moes.architectures.networks -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer JaxDQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE JaxDQNAgent.observation_dtype = %jax_networks.CARTPOLE_OBSERVATION_DTYPE diff --git a/dopamine/labs/moes/configs/dqn_impala.gin b/dopamine/labs/moes/configs/dqn_impala.gin index e1e15c85..7439be04 100644 --- a/dopamine/labs/moes/configs/dqn_impala.gin +++ b/dopamine/labs/moes/configs/dqn_impala.gin @@ -3,7 +3,7 @@ import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.labs.moes.architectures.networks -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 1 diff --git a/dopamine/labs/moes/configs/dqn_lunarlander_moe.gin b/dopamine/labs/moes/configs/dqn_lunarlander_moe.gin index ca630e56..8db8445b 100644 --- a/dopamine/labs/moes/configs/dqn_lunarlander_moe.gin +++ b/dopamine/labs/moes/configs/dqn_lunarlander_moe.gin @@ -3,7 +3,7 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.labs.moes.architectures.networks -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer JaxDQNAgent.observation_shape = %gym_lib.LUNAR_OBSERVATION_SHAPE JaxDQNAgent.observation_dtype = %jax_networks.LUNAR_OBSERVATION_DTYPE diff --git a/dopamine/labs/moes/configs/dqn_mountaincar_moe.gin b/dopamine/labs/moes/configs/dqn_mountaincar_moe.gin index e3d41683..c9483213 100644 --- a/dopamine/labs/moes/configs/dqn_mountaincar_moe.gin +++ b/dopamine/labs/moes/configs/dqn_mountaincar_moe.gin @@ -3,7 +3,7 @@ import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.labs.moes.architectures.networks -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer JaxDQNAgent.observation_shape = %gym_lib.MOUNTAINCAR_OBSERVATION_SHAPE JaxDQNAgent.observation_dtype = %jax_networks.MOUNTAINCAR_OBSERVATION_DTYPE diff --git a/dopamine/labs/moes/configs/full_rainbow.gin b/dopamine/labs/moes/configs/full_rainbow.gin index 3de420cf..68df14b1 100644 --- a/dopamine/labs/moes/configs/full_rainbow.gin +++ b/dopamine/labs/moes/configs/full_rainbow.gin @@ -4,7 +4,7 @@ import dopamine.discrete_domains.run_experiment import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.agents.full_rainbow.full_rainbow_agent import dopamine.labs.moes.architectures.networks -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer JaxDQNAgent.gamma = 0.99 JaxDQNAgent.update_horizon = 3 diff --git a/dopamine/labs/offline_rl/__init__.py b/dopamine/labs/offline_rl/__init__.py new file mode 100644 index 00000000..3157a887 --- /dev/null +++ b/dopamine/labs/offline_rl/__init__.py @@ -0,0 +1,14 @@ +# coding=utf-8 +# Copyright 2021 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/dopamine/labs/offline_rl/fixed_replay.py b/dopamine/labs/offline_rl/fixed_replay.py index 7c791065..263b6b69 100644 --- a/dopamine/labs/offline_rl/fixed_replay.py +++ b/dopamine/labs/offline_rl/fixed_replay.py @@ -16,7 +16,7 @@ from absl import logging from batch_rl.fixed_replay.replay_memory import fixed_replay_buffer -from dopamine.replay_memory import circular_replay_buffer +from dopamine.tf.replay_memory import circular_replay_buffer import gin import numpy as np import tensorflow as tf diff --git a/dopamine/labs/offline_rl/jax/offline_classy_cql_agent.py b/dopamine/labs/offline_rl/jax/offline_classy_cql_agent.py index 381fa2e8..1dcacdae 100644 --- a/dopamine/labs/offline_rl/jax/offline_classy_cql_agent.py +++ b/dopamine/labs/offline_rl/jax/offline_classy_cql_agent.py @@ -41,6 +41,7 @@ class ClassyLoss(enum.StrEnum): HL_GAUSS = 'hl_gauss' TWO_HOT = 'two_hot' + BINARY_CROSSENTROPY = 'binary_crossentropy' SCALAR = 'scalar' # Uses softmax to compute scalar q-values @@ -291,6 +292,12 @@ def __init__( max_value=vmax, apply_symlog=False, ) + case ClassyLoss.BINARY_CROSSENTROPY: + self._transform = classy_transforms.BinaryCrossEntropyLoss( + min_value=vmin, + max_value=vmax, + apply_symlog=False, + ) case ClassyLoss.SCALAR: self._transform = None diff --git a/dopamine/labs/redo/__init__.py b/dopamine/labs/redo/__init__.py new file mode 100644 index 00000000..19d8396a --- /dev/null +++ b/dopamine/labs/redo/__init__.py @@ -0,0 +1,14 @@ +# coding=utf-8 +# Copyright 2023 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/dopamine/labs/redo/weight_recyclers.py b/dopamine/labs/redo/weight_recyclers.py index c02b1bc3..e21401fd 100644 --- a/dopamine/labs/redo/weight_recyclers.py +++ b/dopamine/labs/redo/weight_recyclers.py @@ -218,7 +218,7 @@ def log_intersected_dead_neurons(self, intermediates): Returns: log_dict: dict contains the percentage of intersection """ - score_tree = jax.tree_map(self.estimate_neuron_score, intermediates) + score_tree = jax.tree.map(self.estimate_neuron_score, intermediates) neuron_score_dict = flax.traverse_util.flatten_dict(score_tree, sep='/') if self.prev_neuron_score is None: @@ -295,7 +295,7 @@ def log_dict(score, score_type): ) * 100.0 return log_dict - neuron_score = jax.tree_map(self.estimate_neuron_score, intermediates) + neuron_score = jax.tree.map(self.estimate_neuron_score, intermediates) log_dict_neurons = log_dict(neuron_score, 'feature') return log_dict_neurons @@ -373,12 +373,12 @@ def update_weights(self, intermediates, params, key, opt_state): ) # reset weights weight_random_reset_fn = jax.jit( - functools.partial(jax.tree_map, weight_reinit_random) + functools.partial(jax.tree.map, weight_reinit_random) ) params = weight_random_reset_fn(params, masks, random_keys) # reset mu, nu of adam optimizer for recycled weights. - reset_momentum_fn = jax.jit(functools.partial(jax.tree_map, reset_momentum)) + reset_momentum_fn = jax.jit(functools.partial(jax.tree.map, reset_momentum)) new_mu = reset_momentum_fn(opt_state[0][1], masks) new_nu = reset_momentum_fn(opt_state[0][2], masks) opt_state_list = list(opt_state) @@ -524,7 +524,7 @@ def recycle_dead_neurons(self, intermedieates, params, key, opt_state): scale=self.incoming_scale, weights_type='incoming', ) - weight_random_reset_fn = jax.jit(functools.partial(jax.tree_map, reinit_fn)) + weight_random_reset_fn = jax.jit(functools.partial(jax.tree.map, reinit_fn)) params = weight_random_reset_fn(params, incoming_mask, incoming_random_keys) # reset outgoing weights @@ -539,20 +539,20 @@ def recycle_dead_neurons(self, intermedieates, params, key, opt_state): weights_type='outgoing', ) weight_random_reset_fn = jax.jit( - functools.partial(jax.tree_map, reinit_fn) + functools.partial(jax.tree.map, reinit_fn) ) params = weight_random_reset_fn( params, outgoing_mask, outgoing_random_keys ) elif self.init_method_outgoing == 'zero': weight_zero_reset_fn = jax.jit( - functools.partial(jax.tree_map, weight_reinit_zero) + functools.partial(jax.tree.map, weight_reinit_zero) ) params = weight_zero_reset_fn(params, outgoing_mask) else: raise ValueError(f'Invalid init method: {self.init_method_outgoing}') # reset mu, nu of adam optimizer for recycled weights. - reset_momentum_fn = jax.jit(functools.partial(jax.tree_map, reset_momentum)) + reset_momentum_fn = jax.jit(functools.partial(jax.tree.map, reset_momentum)) new_mu = reset_momentum_fn(opt_state[0][1], incoming_mask) new_mu = reset_momentum_fn(new_mu, outgoing_mask) new_nu = reset_momentum_fn(opt_state[0][2], incoming_mask) diff --git a/dopamine/labs/sac_from_pixels/continuous_networks.py b/dopamine/labs/sac_from_pixels/continuous_networks.py index bbbe1886..70941b0e 100644 --- a/dopamine/labs/sac_from_pixels/continuous_networks.py +++ b/dopamine/labs/sac_from_pixels/continuous_networks.py @@ -101,7 +101,7 @@ class SACConvNetwork(nn.Module): def setup(self): self._encoder = SACEncoderNetwork() - self._sac_network = continuous_networks.SACNetwork( + self._sac_network = continuous_networks.ActorCriticNetwork( self.action_shape, self.num_layers, self.hidden_units, @@ -110,7 +110,7 @@ def setup(self): def __call__( self, state: jnp.ndarray, key: jnp.ndarray, mean_action: bool = True - ) -> continuous_networks.SacOutput: + ) -> continuous_networks.ActorCriticOutput: """Calls the SAC actor/critic networks. This has two important purposes: @@ -136,11 +136,11 @@ def __call__( ) critic_output = self._sac_network.critic(encoding.critic_z, action) - return continuous_networks.SacOutput(actor_output, critic_output) + return continuous_networks.ActorCriticOutput(actor_output, critic_output) def actor( self, state: jnp.ndarray, key: jnp.ndarray - ) -> continuous_networks.SacActorOutput: + ) -> continuous_networks.ActorOutput: """Calls the SAC actor network. This can be called using network_def.apply(..., method=network_def.actor). @@ -159,7 +159,7 @@ def actor( def critic( self, state: jnp.ndarray, action: jnp.ndarray - ) -> continuous_networks.SacCriticOutput: + ) -> continuous_networks.CriticOutput: """Calls the SAC critic network. SAC uses two Q networks to reduce overestimation bias. diff --git a/dopamine/labs/sac_from_pixels/sac_pixels.gin b/dopamine/labs/sac_from_pixels/sac_pixels.gin index 0431b59e..bacdf9cf 100644 --- a/dopamine/labs/sac_from_pixels/sac_pixels.gin +++ b/dopamine/labs/sac_from_pixels/sac_pixels.gin @@ -9,7 +9,7 @@ import dopamine.labs.sac_from_pixels.deepmind_control_lib import dopamine.labs.sac_from_pixels.continuous_networks import dopamine.jax.agents.dqn.dqn_agent import dopamine.jax.agents.sac.sac_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.replay_memory.circular_replay_buffer SACAgent.reward_scale_factor = 0.1 SACAgent.network = @continuous_networks.SACConvNetwork diff --git a/dopamine/utils/example_viz_lib.py b/dopamine/utils/example_viz_lib.py index 91f828b0..ba484dea 100644 --- a/dopamine/utils/example_viz_lib.py +++ b/dopamine/utils/example_viz_lib.py @@ -33,11 +33,11 @@ from absl import logging -from dopamine.agents.dqn import dqn_agent -from dopamine.agents.rainbow import rainbow_agent from dopamine.discrete_domains import atari_lib from dopamine.discrete_domains import iteration_statistics from dopamine.discrete_domains import run_experiment +from dopamine.tf.agents.dqn import dqn_agent +from dopamine.tf.agents.rainbow import rainbow_agent from dopamine.utils import agent_visualizer from dopamine.utils import atari_plotter from dopamine.utils import bar_plotter diff --git a/dopamine/utils/test_utils.py b/dopamine/utils/test_utils.py index 891ceda9..4bb91037 100644 --- a/dopamine/utils/test_utils.py +++ b/dopamine/utils/test_utils.py @@ -32,6 +32,7 @@ def __init__(self, is_jax=False): self.add = mock.Mock() self.add_count = 0 self.sum_tree = mock.Mock() + self._sampling_distribution = mock.Mock() else: with tf.compat.v1.variable_scope( 'MockReplayBuffer', reuse=tf.compat.v1.AUTO_REUSE diff --git a/setup.py b/setup.py index 26cb9cda..ec481956 100644 --- a/setup.py +++ b/setup.py @@ -51,7 +51,7 @@ setup( name='dopamine_rl', - version='4.0.9', + version='4.1.0', description=dopamine_description, long_description=long_description, long_description_content_type='text/markdown', diff --git a/tests/dopamine/continuous_domains/run_experiment_test.py b/tests/dopamine/continuous_domains/run_experiment_test.py index 03fd64eb..47263003 100644 --- a/tests/dopamine/continuous_domains/run_experiment_test.py +++ b/tests/dopamine/continuous_domains/run_experiment_test.py @@ -39,12 +39,8 @@ def setUp(self): self.env.action_space = spaces.Box(0.0, 1.0, (4,)) # Required for creating a SAC agent in create_agent tests. - gin.bind_parameter( - 'circular_replay_buffer.OutOfGraphReplayBuffer.replay_capacity', 10 - ) - gin.bind_parameter( - 'circular_replay_buffer.OutOfGraphReplayBuffer.batch_size', 2 - ) + gin.bind_parameter('ReplayBuffer.max_capacity', 10) + gin.bind_parameter('ReplayBuffer.batch_size', 2) # Required for creating continuous runners. gin.bind_parameter( diff --git a/tests/dopamine/discrete_domains/atari_lib_test.py b/tests/dopamine/discrete_domains/atari_lib_test.py index 690a0280..c52a99a3 100644 --- a/tests/dopamine/discrete_domains/atari_lib_test.py +++ b/tests/dopamine/discrete_domains/atari_lib_test.py @@ -14,10 +14,6 @@ # limitations under the License. """Tests for dopamine.discrete_domains.atari_lib.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - @@ -53,9 +49,9 @@ def fake_make_env(name): # pylint: disable=unnecessary-lambda mock_atari_lib.side_effect = lambda x: 'atari({})'.format(x) # pylint: enable=unnecessary-lambda - game_name = 'Test' + game_name = 'Pong' env = atari_lib.create_atari_environment(game_name) - self.assertEqual('atari(gym(TestNoFrameskip-v0))', env) + self.assertEqual('atari(>>)', env) class MockALE(object): @@ -96,8 +92,8 @@ def step(self, action): is_terminal = self.num_steps >= self.max_steps unused = 0 - self.ale.screen_value -= 2 - return (self.get_observation(), reward, is_terminal, unused) + self.ale.screen_value = max(0, self.ale.screen_value - 2) + return (self.get_observation(), reward, is_terminal, False, unused) def render(self, mode): pass diff --git a/tests/dopamine/discrete_domains/gym_lib_test.py b/tests/dopamine/discrete_domains/gym_lib_test.py index 6c0c73a6..4fa60f55 100644 --- a/tests/dopamine/discrete_domains/gym_lib_test.py +++ b/tests/dopamine/discrete_domains/gym_lib_test.py @@ -14,44 +14,46 @@ # limitations under the License. """Tests for dopamine.discrete_domains.gym_lib.""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - - +from absl.testing import absltest +from absl.testing import parameterized from dopamine.discrete_domains import gym_lib -import tensorflow as tf class MockGymEnvironment(object): - """Mock environment for testing.""" + """Mock environment for testing with Gym/Gymnasium.""" - def __init__(self): + def __init__(self, legacy_gym_api): + self._legacy_gym_api = legacy_gym_api self.observation_space = 'observation_space' self.action_space = 'action_space' self.reward_range = 'reward_range' self.metadata = 'metadata' def reset(self): - return 'reset' + if self._legacy_gym_api: + return 'reset' + return 'reset', 'info' def step(self, unused_action): - return 'obs', 'rew', False, {} + if self._legacy_gym_api: + return 'obs', 'rew', False, {} + return 'obs', 'rew', False, False, {} -class GymPreprocessingTest(tf.test.TestCase): +class GymPreprocessingTest(parameterized.TestCase): - def testAll(self): - env = gym_lib.GymPreprocessing(MockGymEnvironment()) + @parameterized.parameters(True, False) + def testAll(self, use_legacy_gym): + env = gym_lib.GymPreprocessing( + MockGymEnvironment(use_legacy_gym), use_legacy_gym=use_legacy_gym + ) self.assertEqual('observation_space', env.observation_space) self.assertEqual('action_space', env.action_space) self.assertEqual('reward_range', env.reward_range) self.assertEqual('metadata', env.metadata) self.assertEqual('reset', env.reset()) - self.assertAllEqual(['obs', 'rew', False, {}], env.step(0)) + self.assertCountEqual(['obs', 'rew', False, {}], env.step(0)) if __name__ == '__main__': - tf.compat.v1.disable_v2_behavior() - tf.test.main() + absltest.main() diff --git a/tests/dopamine/discrete_domains/run_experiment_test.py b/tests/dopamine/discrete_domains/run_experiment_test.py index a5a25c14..947930dc 100644 --- a/tests/dopamine/discrete_domains/run_experiment_test.py +++ b/tests/dopamine/discrete_domains/run_experiment_test.py @@ -24,14 +24,14 @@ from absl import flags -from dopamine.agents.dqn import dqn_agent -from dopamine.agents.implicit_quantile import implicit_quantile_agent -from dopamine.agents.rainbow import rainbow_agent from dopamine.discrete_domains import checkpointer from dopamine.discrete_domains import logger from dopamine.discrete_domains import run_experiment from dopamine.metrics import collector_dispatcher from dopamine.metrics import statistics_instance +from dopamine.tf.agents.dqn import dqn_agent +from dopamine.tf.agents.implicit_quantile import implicit_quantile_agent +from dopamine.tf.agents.rainbow import rainbow_agent import gin import mock import tensorflow as tf @@ -315,6 +315,41 @@ def testRunOneEpisodeWithLowMaxSteps(self): self.assertEqual(max_steps_per_episode, step_number) self.assertEqual(-1, total_reward) + def testRunOneContinuedEpisode(self): + environment = MockEnvironment() + runner = run_experiment.Runner( + self._test_subdir, + self._create_agent_fn, + lambda: environment, + max_steps_per_episode=None, + ) + step_number, total_reward = runner._run_continued_episode( + start_step_count=0, max_step_count=10 + ) + self.assertEqual(self._agent.step.call_count, 9) + self.assertEqual(self._agent.end_episode.call_count, 1) + self.assertEqual(runner.episode_ended, True) + self.assertEqual(step_number, 10) + # Expected reward will be \sum_{i=0}^{9} (-1)**i * i = -5 + self.assertEqual(-5, total_reward) + + def testRunOneContinuedEpisodeWithLowMaxSteps(self): + environment = MockEnvironment() + runner = run_experiment.Runner( + self._test_subdir, + self._create_agent_fn, + lambda: environment, + max_steps_per_episode=None, + ) + step_number, total_reward = runner._run_continued_episode( + start_step_count=0, max_step_count=1 + ) + self.assertEqual(self._agent.step.call_count, 2) + self.assertEqual(self._agent.end_episode.call_count, 0) + self.assertEqual(runner.episode_ended, False) + self.assertEqual(step_number, 2) + self.assertEqual(-1, total_reward) + def testRunOnePhase(self): max_steps = 10 environment_steps = 2 diff --git a/tests/dopamine/jax/agents/dqn/dqn_agent_test.py b/tests/dopamine/jax/agents/dqn/dqn_agent_test.py index cc3268d3..291ea912 100644 --- a/tests/dopamine/jax/agents/dqn/dqn_agent_test.py +++ b/tests/dopamine/jax/agents/dqn/dqn_agent_test.py @@ -29,7 +29,7 @@ import gin import jax.numpy as jnp import mock -import numpy as onp +import numpy as np FLAGS = flags.FLAGS @@ -50,9 +50,9 @@ def setUp(self): self.observation_shape = dqn_agent.NATURE_DQN_OBSERVATION_SHAPE self.observation_dtype = jnp.uint8 self.stack_size = dqn_agent.NATURE_DQN_STACK_SIZE - self.zero_state = onp.zeros(self.observation_shape + (self.stack_size,)) - gin.bind_parameter('OutOfGraphReplayBuffer.replay_capacity', 100) - gin.bind_parameter('OutOfGraphReplayBuffer.batch_size', 2) + self.zero_state = np.zeros(self.observation_shape + (self.stack_size,)) + gin.bind_parameter('ReplayBuffer.max_capacity', 100) + gin.bind_parameter('ReplayBuffer.batch_size', 2) def _create_test_agent(self, allow_partial_reload=False): @@ -70,7 +70,7 @@ def __call__(self, x): # that it gets picked by the argmax. def custom_init(key, shape, dtype=jnp.float32): del key - to_pick_first_action = onp.zeros(shape, dtype) + to_pick_first_action = np.zeros(shape, dtype) to_pick_first_action[:, 0] = 1 return to_pick_first_action @@ -104,7 +104,7 @@ def custom_init(key, shape, dtype=jnp.float32): def testCreateAgentWithDefaults(self): # Verifies that we can create and train an agent with the default values. agent = dqn_agent.JaxDQNAgent(num_actions=4) - observation = onp.ones([84, 84, 1]) + observation = np.ones([84, 84, 1]) agent.begin_episode(observation) agent.step(reward=1, observation=observation) agent.end_episode(reward=1) @@ -149,15 +149,15 @@ def testBeginEpisode(self): # We fill up the state with 9s. On calling agent.begin_episode the state # should be reset to all 0s. agent.state.fill(9) - first_observation = onp.ones(self.observation_shape + (1,)) + first_observation = np.ones(self.observation_shape + (1,)) self.assertEqual(agent.begin_episode(first_observation), 0) # When the all-1s observation is received, it will be placed at the end of # the state. expected_state = self.zero_state - expected_state[..., -1] = onp.ones(self.observation_shape) - self.assertTrue(onp.array_equal(agent.state, expected_state)) + expected_state[..., -1] = np.ones(self.observation_shape) + self.assertTrue(np.array_equal(agent.state, expected_state)) self.assertTrue( - onp.array_equal(agent._observation, first_observation[:, :, 0]) + np.array_equal(agent._observation, first_observation[:, :, 0]) ) # No training happens in eval mode. self.assertEqual(agent.training_steps, 0) @@ -167,14 +167,14 @@ def testBeginEpisode(self): # Having a low replay memory add_count will prevent any of the # train/prefetch/sync ops from being called. agent._replay.add_count = 0 - second_observation = onp.ones(self.observation_shape + (1,)) * 2 + second_observation = np.ones(self.observation_shape + (1,)) * 2 agent.begin_episode(second_observation) # The agent's state will be reset, so we will only be left with the all-2s # observation. - expected_state[:, :, -1] = onp.full(self.observation_shape, 2) - self.assertTrue(onp.array_equal(agent.state, expected_state)) + expected_state[:, :, -1] = np.full(self.observation_shape, 2) + self.assertTrue(np.array_equal(agent.state, expected_state)) self.assertTrue( - onp.array_equal(agent._observation, second_observation[:, :, 0]) + np.array_equal(agent._observation, second_observation[:, :, 0]) ) # training_steps is incremented since we set eval_mode to False. self.assertEqual(agent.training_steps, 1) @@ -185,7 +185,7 @@ def testStepEval(self): Specifically, the action returned, and confirm no training is happening. """ agent = self._create_test_agent() - base_observation = onp.ones(self.observation_shape + (1,)) + base_observation = np.ones(self.observation_shape + (1,)) # This will reset state and choose a first action. agent.begin_episode(base_observation) # We mock the replay buffer to verify how the agent interacts with it. @@ -200,15 +200,15 @@ def testStepEval(self): self.assertEqual(agent.step(reward=1, observation=observation), 0) stack_pos = step - num_steps - 1 if stack_pos >= -self.stack_size: - expected_state[:, :, stack_pos] = onp.full(self.observation_shape, step) - self.assertTrue(onp.array_equal(agent.state, expected_state)) + expected_state[:, :, stack_pos] = np.full(self.observation_shape, step) + self.assertTrue(np.array_equal(agent.state, expected_state)) self.assertTrue( - onp.array_equal( + np.array_equal( agent._last_observation, - onp.ones(self.observation_shape) * (num_steps - 1), + np.ones(self.observation_shape) * (num_steps - 1), ) ) - self.assertTrue(onp.array_equal(agent._observation, observation[:, :, 0])) + self.assertTrue(np.array_equal(agent._observation, observation[:, :, 0])) # No training happens in eval mode. self.assertEqual(agent.training_steps, 0) # No transitions are added in eval mode. @@ -221,7 +221,7 @@ def testStepTrain(self): """ agent = self._create_test_agent() agent.eval_mode = False - base_observation = onp.ones(self.observation_shape + (1,)) + base_observation = np.ones(self.observation_shape + (1,)) # We mock the replay buffer to verify how the agent interacts with it. agent._replay = test_utils.MockReplayBuffer(is_jax=True) # This will reset state and choose a first action. @@ -238,21 +238,23 @@ def testStepTrain(self): self.assertEqual(agent.step(reward=1, observation=observation), 0) stack_pos = step - num_steps - 1 if stack_pos >= -self.stack_size: - expected_state[:, :, stack_pos] = onp.full(self.observation_shape, step) + expected_state[:, :, stack_pos] = np.full(self.observation_shape, step) self.assertEqual(agent._replay.add.call_count, step) mock_args, _ = agent._replay.add.call_args - self.assertTrue(onp.array_equal(last_observation[:, :, 0], mock_args[0])) - self.assertEqual(0, mock_args[1]) # Action selected. - self.assertEqual(1, mock_args[2]) # Reward received. - self.assertFalse(mock_args[3]) # is_terminal - self.assertTrue(onp.array_equal(agent.state, expected_state)) + self.assertTrue( + np.array_equal(last_observation[:, :, 0], mock_args[0].observation) + ) + self.assertEqual(0, mock_args[0].action) # Action selected. + self.assertEqual(1, mock_args[0].reward) # Reward received. + self.assertFalse(mock_args[0].is_terminal) # is_terminal + self.assertTrue(np.array_equal(agent.state, expected_state)) self.assertTrue( - onp.array_equal( + np.array_equal( agent._last_observation, - onp.full(self.observation_shape, num_steps - 1), + np.full(self.observation_shape, num_steps - 1), ) ) - self.assertTrue(onp.array_equal(agent._observation, observation[:, :, 0])) + self.assertTrue(np.array_equal(agent._observation, observation[:, :, 0])) # We expect one more than num_steps because of the call to begin_episode. self.assertEqual(agent.training_steps, num_steps + 1) self.assertEqual(agent._replay.add.call_count, num_steps) @@ -260,10 +262,12 @@ def testStepTrain(self): agent.end_episode(reward=1) self.assertEqual(agent._replay.add.call_count, num_steps + 1) mock_args, _ = agent._replay.add.call_args - self.assertTrue(onp.array_equal(observation[:, :, 0], mock_args[0])) - self.assertEqual(0, mock_args[1]) # Action selected. - self.assertEqual(1, mock_args[2]) # Reward received. - self.assertTrue(mock_args[3]) # is_terminal + self.assertTrue( + np.array_equal(observation[:, :, 0], mock_args[0].observation) + ) + self.assertEqual(0, mock_args[0].action) # Action selected. + self.assertEqual(1, mock_args[0].reward) # Reward received. + self.assertTrue(mock_args[0].is_terminal) # is_terminal def testNonTupleObservationShape(self): with self.assertRaises(AssertionError): @@ -274,10 +278,10 @@ def _custom_shapes_test(self, shape, dtype, stack_size): self.observation_shape = shape self.observation_dtype = dtype self.stack_size = stack_size - self.zero_state = onp.zeros(shape + (stack_size,)) + self.zero_state = np.zeros(shape + (stack_size,)) agent = self._create_test_agent() agent.eval_mode = False - base_observation = onp.ones(self.observation_shape + (1,)) + base_observation = np.ones(self.observation_shape + (1,)) # We mock the replay buffer to verify how the agent interacts with it. agent._replay = test_utils.MockReplayBuffer(is_jax=True) # This will reset state and choose a first action. @@ -294,21 +298,23 @@ def _custom_shapes_test(self, shape, dtype, stack_size): self.assertEqual(agent.step(reward=1, observation=observation), 0) stack_pos = step - num_steps - 1 if stack_pos >= -self.stack_size: - expected_state[..., stack_pos] = onp.full(self.observation_shape, step) + expected_state[..., stack_pos] = np.full(self.observation_shape, step) self.assertEqual(agent._replay.add.call_count, step) mock_args, _ = agent._replay.add.call_args - self.assertTrue(onp.array_equal(last_observation[..., 0], mock_args[0])) - self.assertEqual(0, mock_args[1]) # Action selected. - self.assertEqual(1, mock_args[2]) # Reward received. - self.assertFalse(mock_args[3]) # is_terminal - self.assertTrue(onp.array_equal(agent.state, expected_state)) + self.assertTrue( + np.array_equal(last_observation[..., 0], mock_args[0].observation) + ) + self.assertEqual(0, mock_args[0].action) # Action selected. + self.assertEqual(1, mock_args[0].reward) # Reward received. + self.assertFalse(mock_args[0].is_terminal) # is_terminal + self.assertTrue(np.array_equal(agent.state, expected_state)) self.assertTrue( - onp.array_equal( + np.array_equal( agent._last_observation, - onp.full(self.observation_shape, num_steps - 1), + np.full(self.observation_shape, num_steps - 1), ) ) - self.assertTrue(onp.array_equal(agent._observation, observation[..., 0])) + self.assertTrue(np.array_equal(agent._observation, observation[..., 0])) # We expect one more than num_steps because of the call to begin_episode. self.assertEqual(agent.training_steps, num_steps + 1) self.assertEqual(agent._replay.add.call_count, num_steps) @@ -316,10 +322,12 @@ def _custom_shapes_test(self, shape, dtype, stack_size): agent.end_episode(reward=1) self.assertEqual(agent._replay.add.call_count, num_steps + 1) mock_args, _ = agent._replay.add.call_args - self.assertTrue(onp.array_equal(observation[..., 0], mock_args[0])) - self.assertEqual(0, mock_args[1]) # Action selected. - self.assertEqual(1, mock_args[2]) # Reward received. - self.assertTrue(mock_args[3]) # is_terminal + self.assertTrue( + np.array_equal(observation[..., 0], mock_args[0].observation) + ) + self.assertEqual(0, mock_args[0].action) # Action selected. + self.assertEqual(1, mock_args[0].reward) # Reward received. + self.assertTrue(mock_args[0].is_terminal) # is_terminal def testStepTrainCustomObservationShapes(self): custom_shapes = [(1,), (4, 4), (6, 1), (1, 6), (1, 1, 6), (6, 6, 6, 6)] @@ -396,7 +404,7 @@ def testLinearlyDecayingEpsilon(self): (decay_period + warmup_steps + 1, epsilon), ] # step > decay+warmup for step, expected_epsilon in steps_schedule: - onp.testing.assert_almost_equal( + np.testing.assert_almost_equal( dqn_agent.linearly_decaying_epsilon( decay_period, step, warmup_steps, epsilon ), diff --git a/tests/dopamine/jax/agents/full_rainbow/full_rainbow_agent_test.py b/tests/dopamine/jax/agents/full_rainbow/full_rainbow_agent_test.py index b53213aa..e4143981 100644 --- a/tests/dopamine/jax/agents/full_rainbow/full_rainbow_agent_test.py +++ b/tests/dopamine/jax/agents/full_rainbow/full_rainbow_agent_test.py @@ -40,8 +40,9 @@ def setUp(self): self.zero_state = onp.zeros( (1,) + self.observation_shape + (self.stack_size,) ) - gin.bind_parameter('OutOfGraphPrioritizedReplayBuffer.replay_capacity', 100) - gin.bind_parameter('OutOfGraphPrioritizedReplayBuffer.batch_size', 2) + gin.bind_parameter('ReplayBuffer.max_capacity', 100) + gin.bind_parameter('ReplayBuffer.batch_size', 2) + gin.bind_parameter('PrioritizedSamplingDistribution.max_capacity', 100) gin.bind_parameter('JaxDQNAgent.min_replay_history', 32) gin.bind_parameter('JaxDQNAgent.epsilon_eval', 0.0) gin.bind_parameter('JaxDQNAgent.epsilon_decay_period', 90) @@ -246,7 +247,7 @@ def testStoreTransitionWithUniformSampling(self): agent._store_transition(dummy_frame, 0, 0, False) agent._store_transition(dummy_frame, 0, 0, False, priority=10.0) agent._store_transition(dummy_frame, 0, 0, False) - returned_priorities = agent._replay.get_priority( + returned_priorities = agent._replay._sampling_distribution._sum_tree.get( onp.arange(self.stack_size - 1, self.stack_size + 2, dtype=onp.int32) ) expected_priorities = [1.0, 10.0, 1.0] @@ -261,7 +262,7 @@ def testStoreTransitionWithPrioritizedSampling(self): agent._store_transition(dummy_frame, 0, 0, False) agent._store_transition(dummy_frame, 0, 0, False, priority=10.0) agent._store_transition(dummy_frame, 0, 0, False) - returned_priorities = agent._replay.get_priority( + returned_priorities = agent._replay._sampling_distribution._sum_tree.get( onp.arange(self.stack_size - 1, self.stack_size + 2, dtype=onp.int32) ) expected_priorities = [1.0, 10.0, 10.0] diff --git a/tests/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent_test.py b/tests/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent_test.py index 93d7b5ab..7f3e02a7 100644 --- a/tests/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent_test.py +++ b/tests/dopamine/jax/agents/implicit_quantile/implicit_quantile_agent_test.py @@ -42,8 +42,9 @@ def setUp(self): self.ones_state = onp.ones( (1,) + self.observation_shape + (self.stack_size,) ) - gin.bind_parameter('OutOfGraphReplayBuffer.replay_capacity', 100) - gin.bind_parameter('OutOfGraphReplayBuffer.batch_size', 2) + gin.bind_parameter('ReplayBuffer.max_capacity', 100) + gin.bind_parameter('ReplayBuffer.batch_size', 2) + gin.bind_parameter('PrioritizedSamplingDistribution.max_capacity', 100) def _create_test_agent(self): # This dummy network allows us to deterministically anticipate that the diff --git a/tests/dopamine/jax/agents/ppo/ppo_agent_test.py b/tests/dopamine/jax/agents/ppo/ppo_agent_test.py new file mode 100644 index 00000000..9924046b --- /dev/null +++ b/tests/dopamine/jax/agents/ppo/ppo_agent_test.py @@ -0,0 +1,359 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for dopamine.jax.agents.ppo.ppo_agent.""" + +from typing import Tuple, Union +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax.agents.ppo import ppo_agent +import flax +import gin +import jax +import jax.numpy as jnp +import numpy as np + + +OBSERVATION_SHAPE = (8,) + + +def create_agent( + action_shape: Union[Tuple[int, ...], int] = 4, # pytype: disable=annotation-type-mismatch # numpy-scalars + eval_mode: bool = False, + update_period: int = 100, + seed: int = 0, + observation_shape: Tuple[int, ...] = OBSERVATION_SHAPE, + stack_size: int = 1, +) -> ppo_agent.PPOAgent: + return ppo_agent.PPOAgent( + action_shape=action_shape, + action_limits=None, + observation_shape=observation_shape, + stack_size=stack_size, + update_period=update_period, + eval_mode=eval_mode, + seed=seed, + ) + + +def get_agent_params( + agent: ppo_agent.PPOAgent, +) -> flax.core.FrozenDict: + return agent.network_params + + +class PPOAgentTest(parameterized.TestCase): + + def assertAgentParametersEqual( + self, agent1: ppo_agent.PPOAgent, agent2: ppo_agent.PPOAgent + ): + agent1_params = get_agent_params(agent1) + agent2_params = get_agent_params(agent2) + + agent1_params, agent1_structure = jax.tree.flatten(agent1_params) + agent2_params, agent2_structure = jax.tree.flatten(agent2_params) + + self.assertEqual( + agent1_structure, agent2_structure, 'Parameter structures do not match.' + ) + + for param1, param2 in zip(agent1_params, agent2_params): + if (param1 != param2).any(): + self.fail(f'Parameters are not equal: {param1}, {param2}') + + def assertAgentParametersNotEqual( + self, agent1: ppo_agent.PPOAgent, agent2: ppo_agent.PPOAgent + ): + agent1_params = get_agent_params(agent1) + agent2_params = get_agent_params(agent2) + + agent1_params, agent1_structure = jax.tree.flatten(agent1_params) + agent2_params, agent2_structure = jax.tree.flatten(agent2_params) + + if agent1_structure != agent2_structure: + self.fail('Parameter structures are not comparable.') + + for param1, param2 in zip(agent1_params, agent2_params): + if (param1 != param2).any(): + return + + # If you get here, all parameters are equal. + self.fail(f'All parameters are equal: {agent1_params}, {agent2_params}') + + def setUp(self): + super(PPOAgentTest, self).setUp() + gin.bind_parameter('ReplayBuffer.max_capacity', 100) + gin.bind_parameter('ReplayBuffer.batch_size', 100) + + @parameterized.named_parameters( + dict(testcase_name='Eval', eval_mode=True), + dict(testcase_name='NoEval', eval_mode=False), + ) + def test_integer_shaped_actions_match_shapes(self, eval_mode: bool): + action_shape = 4 + agent = create_agent(action_shape=action_shape, eval_mode=eval_mode) + observation = np.zeros(OBSERVATION_SHAPE) + + action1 = agent.begin_episode(observation) + action2 = agent.step(reward=0.0, observation=observation) + + self.assertIsInstance(action1, np.ndarray) + self.assertIsInstance(action2, np.ndarray) + self.assertEqual(action1.shape, (action_shape,)) + self.assertEqual(action2.shape, (action_shape,)) + + @parameterized.named_parameters( + dict(testcase_name='Eval1', action_shape=(4,), eval_mode=True), + dict(testcase_name='NoEval1', action_shape=(4,), eval_mode=False), + dict(testcase_name='Eval2', action_shape=(4, 3), eval_mode=True), + dict(testcase_name='NoEval2', action_shape=(4, 3), eval_mode=False), + ) + def test_tuple_shaped_actions_match_shapes( + self, action_shape: Tuple[int, ...], eval_mode: bool + ): + agent = create_agent(action_shape=action_shape, eval_mode=eval_mode) + observation = np.zeros(OBSERVATION_SHAPE) + + action1 = agent.begin_episode(observation) + action2 = agent.step(reward=0.0, observation=observation) + + self.assertIsInstance(action1, np.ndarray) + self.assertIsInstance(action2, np.ndarray) + self.assertEqual(tuple(action1.shape), action_shape) + self.assertEqual(tuple(action2.shape), action_shape) + + def test_restore_agent_from_bundle_restores_parameters(self): + agent1 = create_agent(action_shape=(4, 3), seed=123) + agent2 = create_agent(action_shape=(4, 3), seed=456) + + agent1._replay.save = mock.create_autospec( + agent1._replay.save, spec_set=True + ) + agent2._replay.load = mock.create_autospec( + agent2._replay.load, spec_set=True + ) + + # Make sure the parameters are initialized differently + self.assertAgentParametersNotEqual(agent1, agent2) + + # No data is written to file, but the directory must exist + tempdir = self.create_tempdir() + bundle = agent1.bundle_and_checkpoint(tempdir, 0) + self.assertIsNotNone(bundle) # Ensures a bundle was returned + agent2.unbundle(tempdir, 0, bundle) + + self.assertAgentParametersEqual(agent1, agent2) + + def test_calculate_advantages_and_returns_shapes(self): + batch_shape = (10,) + q_values = jnp.zeros(batch_shape) + next_q_value = jnp.array(0.0) + rewards = jnp.zeros(batch_shape) + terminals = jnp.zeros(batch_shape) + gamma = 0.99 + lambd = 0.95 + advantages, returns = ppo_agent.calculate_advantages_and_returns( + q_values, + next_q_value, + rewards, + terminals, + gamma, + lambd, + ) + self.assertEqual(advantages.shape, batch_shape) + self.assertEqual(advantages.dtype, jnp.float32) + self.assertEqual(returns.shape, batch_shape) + self.assertEqual(returns.dtype, jnp.float32) + + @parameterized.named_parameters( + dict( + testcase_name='infinite_time_rewards', + discounts=np.array([5.0, 4.0, 3.0, 2.0, 1.0]), + gamma=1.0, + lambda_=1.0, + ), + dict( + testcase_name='immediate_rewards', + discounts=np.array([1.0, 1.0, 1.0, 1.0, 1.0]), + gamma=0.0, + lambda_=0.0, + ), + dict( + testcase_name='lambda_discount', + discounts=np.array([4.900995, 3.940399, 2.9701, 1.99, 1.0]), + gamma=1.0, + lambda_=0.99, + ), + dict( + testcase_name='gamma_discount', + discounts=np.array([4.900995, 3.940399, 2.9701, 1.99, 1.0]), + gamma=0.99, + lambda_=1.0, + ), + dict( + testcase_name='gamma_and_lambda_discount', + discounts=np.array([4.80492, 3.882176, 2.940696, 1.9801, 1.0]), + gamma=0.99, + lambda_=0.99, + ), + ) + def test_calculate_advantages_and_returns_discounting( + self, discounts, gamma, lambda_ + ): + batch_shape = (5,) + q_values = jnp.zeros(batch_shape) + next_q_value = jnp.array(0.0) + # reward is 1.0 for all timesteps + rewards = jnp.ones(batch_shape) + terminals = jnp.zeros(batch_shape) + advantages, returns = ppo_agent.calculate_advantages_and_returns( + q_values, + next_q_value, + rewards, + terminals, + gamma, + lambda_, + ) + np.testing.assert_array_almost_equal(returns, discounts) + np.testing.assert_array_almost_equal(advantages, discounts) + + @parameterized.named_parameters( + dict( + testcase_name='full_q_values', + q_values=np.array([1.0, 2.0, 3.0, 4.0, 5.0]), + next_q_value=np.array(6.0), + expected_returns=np.array([6.0, 6.0, 6.0, 6.0, 6.0]), + expected_advantages=np.array([5.0, 4.0, 3.0, 2.0, 1.0]), + ), + dict( + testcase_name='partial_q_values', + q_values=np.array([0.0, 0.0, 0.0, 1.0, 2.0]), + next_q_value=np.array(3.0), + expected_returns=np.array([3.0, 3.0, 3.0, 3.0, 3.0]), + expected_advantages=np.array([3.0, 3.0, 3.0, 2.0, 1.0]), + ), + dict( + testcase_name='only_last_q_value', + q_values=np.array([0.0, 0.0, 0.0, 0.0, 0.0]), + next_q_value=np.array(1.0), + expected_returns=np.array([1.0, 1.0, 1.0, 1.0, 1.0]), + expected_advantages=np.array([1.0, 1.0, 1.0, 1.0, 1.0]), + ), + dict( + testcase_name='zero_q_values', + q_values=np.array([0.0, 0.0, 0.0, 0.0, 0.0]), + next_q_value=np.array(0.0), + expected_returns=np.array([0.0, 0.0, 0.0, 0.0, 0.0]), + expected_advantages=np.array([0.0, 0.0, 0.0, 0.0, 0.0]), + ), + ) + def test_calculate_advantages_and_returns_q_value_calculations( + self, q_values, next_q_value, expected_returns, expected_advantages + ): + batch_shape = (5,) + rewards = jnp.zeros(batch_shape) + terminals = jnp.zeros(batch_shape) + gamma = 1.0 + lambda_ = 1.0 + advantages, returns = ppo_agent.calculate_advantages_and_returns( + q_values, + next_q_value, + rewards, + terminals, + gamma, + lambda_, + ) + np.testing.assert_array_almost_equal(returns, expected_returns) + np.testing.assert_array_almost_equal(advantages, expected_advantages) + + def test_create_minibatches_and_shuffle_shapes(self): + action_shape = (4,) + batch_shape = (10,) + batch_size = 5 + states = jnp.zeros(batch_shape + OBSERVATION_SHAPE) + actions = jnp.zeros(batch_shape + action_shape) + returns = jnp.zeros(batch_shape) + advantages = jnp.zeros(batch_shape) + log_probability = jnp.zeros(batch_shape) + q_values = jnp.zeros(batch_shape) + key = jax.random.PRNGKey(0) + ( + num_batches, + states, + actions, + returns, + advantages, + log_probability, + q_values, + ) = ppo_agent.create_minibatches_and_shuffle( + states, + actions, + returns, + advantages, + log_probability, + q_values, + batch_size, + key, + ) + self.assertEqual( + states.shape, (num_batches, batch_size) + OBSERVATION_SHAPE + ) + self.assertEqual(actions.shape, (num_batches, batch_size) + action_shape) + self.assertEqual(returns.shape, (num_batches, batch_size)) + self.assertEqual(advantages.shape, (num_batches, batch_size)) + self.assertEqual(log_probability.shape, (num_batches, batch_size)) + self.assertEqual(q_values.shape, (num_batches, batch_size)) + + def test_create_minibatches_and_shuffle_incorrect_batch_size(self): + action_shape = (4,) + batch_shape = (11,) + batch_size = 5 + states = jnp.zeros(batch_shape + OBSERVATION_SHAPE) + actions = jnp.zeros(batch_shape + action_shape) + returns = jnp.zeros(batch_shape) + advantages = jnp.zeros(batch_shape) + log_probability = jnp.zeros(batch_shape) + q_values = jnp.zeros(batch_shape) + key = jax.random.PRNGKey(0) + with self.assertRaises(AssertionError): + ppo_agent.create_minibatches_and_shuffle( + states, + actions, + returns, + advantages, + log_probability, + q_values, + batch_size, + key, + ) + + @parameterized.named_parameters( + dict(testcase_name='1dim', action_shape=(4,)), + dict(testcase_name='2dim', action_shape=(4, 3)), + ) + def test_select_action_shapes(self, action_shape): + agent = create_agent(action_shape=action_shape) + state = jnp.zeros(OBSERVATION_SHAPE) + rng = jax.random.PRNGKey(0) + rng, action = ppo_agent.select_action( + agent.network_def, agent.network_params, state, rng + ) + self.assertEqual(action.shape, action_shape) + self.assertEqual(rng.shape, (2,)) + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/jax/agents/quantile/quantile_agent_test.py b/tests/dopamine/jax/agents/quantile/quantile_agent_test.py index feedcd36..0fda3979 100644 --- a/tests/dopamine/jax/agents/quantile/quantile_agent_test.py +++ b/tests/dopamine/jax/agents/quantile/quantile_agent_test.py @@ -45,8 +45,9 @@ def setUp(self): self.zero_state = onp.zeros( (1,) + self.observation_shape + (self.stack_size,) ) - gin.bind_parameter('OutOfGraphPrioritizedReplayBuffer.replay_capacity', 100) - gin.bind_parameter('OutOfGraphPrioritizedReplayBuffer.batch_size', 2) + gin.bind_parameter('ReplayBuffer.max_capacity', 100) + gin.bind_parameter('ReplayBuffer.batch_size', 2) + gin.bind_parameter('PrioritizedSamplingDistribution.max_capacity', 100) def _create_test_agent(self): """Keras network for tests.""" diff --git a/tests/dopamine/jax/agents/rainbow/rainbow_agent_test.py b/tests/dopamine/jax/agents/rainbow/rainbow_agent_test.py index c9957f65..39a32306 100644 --- a/tests/dopamine/jax/agents/rainbow/rainbow_agent_test.py +++ b/tests/dopamine/jax/agents/rainbow/rainbow_agent_test.py @@ -146,8 +146,9 @@ def setUp(self): self.zero_state = onp.zeros( (1,) + self.observation_shape + (self.stack_size,) ) - gin.bind_parameter('OutOfGraphPrioritizedReplayBuffer.replay_capacity', 100) - gin.bind_parameter('OutOfGraphPrioritizedReplayBuffer.batch_size', 2) + gin.bind_parameter('ReplayBuffer.max_capacity', 100) + gin.bind_parameter('ReplayBuffer.batch_size', 2) + gin.bind_parameter('PrioritizedSamplingDistribution.max_capacity', 100) def _create_test_agent(self): # This dummy network allows us to deterministically anticipate that @@ -345,7 +346,7 @@ def testStoreTransitionWithUniformSampling(self): agent._store_transition(dummy_frame, 0, 0, False) agent._store_transition(dummy_frame, 0, 0, False, priority=10.0) agent._store_transition(dummy_frame, 0, 0, False) - returned_priorities = agent._replay.get_priority( + returned_priorities = agent._replay._sampling_distribution._sum_tree.get( onp.arange(self.stack_size - 1, self.stack_size + 2, dtype=onp.int32) ) expected_priorities = [1.0, 10.0, 1.0] @@ -360,7 +361,7 @@ def testStoreTransitionWithPrioritizedSamplingy(self): agent._store_transition(dummy_frame, 0, 0, False) agent._store_transition(dummy_frame, 0, 0, False, priority=10.0) agent._store_transition(dummy_frame, 0, 0, False) - returned_priorities = agent._replay.get_priority( + returned_priorities = agent._replay._sampling_distribution._sum_tree.get( onp.arange(self.stack_size - 1, self.stack_size + 2, dtype=onp.int32) ) expected_priorities = [1.0, 10.0, 10.0] diff --git a/tests/dopamine/jax/agents/sac/sac_agent_test.py b/tests/dopamine/jax/agents/sac/sac_agent_test.py index 769cd7cf..b0e42175 100644 --- a/tests/dopamine/jax/agents/sac/sac_agent_test.py +++ b/tests/dopamine/jax/agents/sac/sac_agent_test.py @@ -24,6 +24,8 @@ from absl.testing import absltest from absl.testing import parameterized from dopamine.jax.agents.sac import sac_agent +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import samplers import flax import gin import jax @@ -49,19 +51,26 @@ def get_mock_batch( mock_action = mock_action / np.max(mock_action) # Squish to action limits mock_reward = np.asarray(1.0) mock_terminal = np.asarray(0.0) + mock_episode_end = np.asarray(0.0) mock_indices = np.asarray(0) mock_sampling_prob = np.asarray(1.0) - return ( - np.stack([mock_observation for _ in range(batch_size)], axis=0), - np.stack([mock_action for _ in range(batch_size)], axis=0), - np.stack([mock_reward for _ in range(batch_size)], axis=0), - np.stack([mock_observation for _ in range(batch_size)], axis=0), - np.stack([mock_action for _ in range(batch_size)], axis=0), - np.stack([mock_reward for _ in range(batch_size)], axis=0), - np.stack([mock_terminal for _ in range(batch_size)], axis=0), - np.stack([mock_indices for _ in range(batch_size)], axis=0), - np.stack([mock_sampling_prob for _ in range(batch_size)], axis=0), + return elements.ReplayElement( + state=np.stack([mock_observation for _ in range(batch_size)], axis=0), + action=np.stack([mock_action for _ in range(batch_size)], axis=0), + reward=np.stack([mock_reward for _ in range(batch_size)], axis=0), + next_state=np.stack( + [mock_observation for _ in range(batch_size)], axis=0 + ), + is_terminal=np.stack([mock_terminal for _ in range(batch_size)], axis=0), + episode_end=np.stack( + [mock_episode_end for _ in range(batch_size)], axis=0 + ), + ), samplers.PrioritizedSampleMetadata( + keys=np.stack([mock_indices for _ in range(batch_size)], axis=0), + probabilities=np.stack( + [mock_sampling_prob for _ in range(batch_size)], axis=0 + ), ) @@ -102,8 +111,8 @@ def assertAgentParametersEqual( agent1_params = get_agent_params(agent1) agent2_params = get_agent_params(agent2) - agent1_params, agent1_structure = jax.tree_flatten(agent1_params) - agent2_params, agent2_structure = jax.tree_flatten(agent2_params) + agent1_params, agent1_structure = jax.tree.flatten(agent1_params) + agent2_params, agent2_structure = jax.tree.flatten(agent2_params) self.assertEqual( agent1_structure, agent2_structure, 'Parameter structures do not match.' @@ -119,8 +128,8 @@ def assertAgentParametersNotEqual( agent1_params = get_agent_params(agent1) agent2_params = get_agent_params(agent2) - agent1_params, agent1_structure = jax.tree_flatten(agent1_params) - agent2_params, agent2_structure = jax.tree_flatten(agent2_params) + agent1_params, agent1_structure = jax.tree.flatten(agent1_params) + agent2_params, agent2_structure = jax.tree.flatten(agent2_params) if agent1_structure != agent2_structure: self.fail('Parameter structures are not comparable.') @@ -134,8 +143,8 @@ def assertAgentParametersNotEqual( def setUp(self): super(SacAgentTest, self).setUp() - gin.bind_parameter('OutOfGraphReplayBuffer.replay_capacity', 100) - gin.bind_parameter('OutOfGraphReplayBuffer.batch_size', 2) + gin.bind_parameter('ReplayBuffer.max_capacity', 100) + gin.bind_parameter('ReplayBuffer.batch_size', 2) random.seed(0) # random is used for sampling experiences from buffer. @parameterized.named_parameters( @@ -188,7 +197,7 @@ def testAgentParametersUpdateWhenTrained(self, action_shape: Tuple[int, ...]): ) observation = np.full(OBSERVATION_SHAPE, 1.0, dtype=np.float32) - agent._replay.sample_transition_batch = mock.MagicMock( + agent._replay.sample = mock.MagicMock( return_value=get_mock_batch(action_shape) ) agent._replay.add_count = 10 @@ -213,7 +222,7 @@ def testAgentParametersNotUpdatedDuringEval(self): ) observation = np.full(OBSERVATION_SHAPE, 1.0, dtype=np.float32) - agent._replay.sample_transition_batch = mock.MagicMock( + agent._replay.sample = mock.MagicMock( return_value=get_mock_batch(action_shape) ) agent._replay.add_count = 10 @@ -262,7 +271,7 @@ def testAgentTrainsWithImageObservations(self): ) observation = np.full(IMG_OBSERVATION_SHAPE, 1.0, dtype=np.float32) - agent._replay.sample_transition_batch = mock.MagicMock( + agent._replay.sample = mock.MagicMock( return_value=get_mock_batch( action_shape=action_shape, observation_shape=IMG_OBSERVATION_SHAPE, diff --git a/tests/dopamine/jax/checkpointers_test.py b/tests/dopamine/jax/checkpointers_test.py new file mode 100644 index 00000000..cbb178ee --- /dev/null +++ b/tests/dopamine/jax/checkpointers_test.py @@ -0,0 +1,112 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Checkpointer test.""" + +from typing import Any, Dict + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax import checkpointers +from etils import epath +import numpy as np +from orbax import checkpoint as orbax + + +class CheckpointTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.tmpdir = epath.Path(self.create_tempdir('checkpoint').full_path) + + def make_checkpoint_manager(self) -> orbax.CheckpointManager: + return orbax.CheckpointManager( + self.tmpdir, + checkpointers=checkpointers.Checkpointer(), + ) + + def testInvalidCheckpointProtocol(self): + """Test an invalid checkpointable.""" + + class A: + ... + + checkpoint_manager = self.make_checkpoint_manager() + with self.assertRaisesRegex( + NotImplementedError, 'must implement Checkpointable$' + ): + checkpoint_manager.save(0, A()) + + @parameterized.parameters( + (1,), + (1.0,), + (True,), + (123456789101112131415161718,), + ('string',), + ({'key1': 1, 'key2': True, 'key3': 'string'},), + ([1, 1.0, True, 'string'],), + ([[1, 1.0, True, 'string'], [2, 2.0, True, 'string']],), + # Numpy array with different orders + (np.array([1, 2, 3], order='F'),), + (np.array([1, 2, 3], order='C'),), + (np.array([1, 2, 3]),), + (np.zeros((4, 4, 4), order='F'),), + (np.zeros((4, 4, 4), order='C'),), + (np.zeros((4, 4, 4)),), + # Structured array + ( + np.array( + [('A', 1, 1.0), ('B', 2, 2.0)], + dtype=[('string', 'U1'), ('int', 'i4'), ('float', 'f4')], + ), + ), + ) + def testCheckpointable(self, data: Any): + """Test various data types with checkpointable.""" + + class A: + + def to_state_dict(self) -> Dict[str, Any]: + return {'data': data} + + def from_state_dict(self, state_dict: Dict[str, Any]) -> None: + self.data = state_dict['data'] + + checkpoint_manager = self.make_checkpoint_manager() + + a = A() + checkpoint_manager.save(0, a) + + # Restore to state dict + restored = checkpoint_manager.restore(0) + self.assertIsInstance(restored, dict) + self.assertIn('data', restored) + if isinstance(data, np.ndarray): + np.testing.assert_array_equal(restored['data'], data) + else: + self.assertEqual(restored['data'], data) + + restored = checkpoint_manager.restore(0, a) + self.assertIsInstance(restored, A) + # Object should have been deep copied + self.assertNotEqual(id(a), id(restored)) + assert hasattr(restored, 'data') + if isinstance(data, np.ndarray): + np.testing.assert_array_equal(restored.data, data) + else: + self.assertEqual(restored.data, data) + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/jax/continuous_networks_test.py b/tests/dopamine/jax/continuous_networks_test.py index 58b229f2..8a4775db 100644 --- a/tests/dopamine/jax/continuous_networks_test.py +++ b/tests/dopamine/jax/continuous_networks_test.py @@ -15,12 +15,68 @@ """Tests for networks.""" from absl.testing import absltest +from absl.testing import parameterized from dopamine.jax import continuous_networks +from flax import linen as nn import jax from jax import numpy as jnp +import numpy as np -class SacCriticNetworkTest(absltest.TestCase): +class ActorNetworkTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.key = jax.random.PRNGKey(0) + self.example_state = jnp.zeros((8,), dtype=jnp.float32) + self.action_shape = (4,) + + def test_actor_network_outputs_correct_shaped_values(self): + network_def = continuous_networks.ActorNetwork(self.action_shape) + params = network_def.init(self.key, self.example_state, self.key) + + mode, sampled_action, action_probability = network_def.apply( + params, self.example_state, self.key + ) + + self.assertEqual(mode.shape, self.action_shape) + self.assertEqual(sampled_action.shape, self.action_shape) + self.assertEqual(action_probability.shape, ()) + + def test_network_has_specified_number_of_layers(self): + num_layers = 5 + network_def = continuous_networks.ActorNetwork( + self.action_shape, num_layers + ) + params = network_def.init(self.key, self.example_state, self.key) + + # Checks which dense layers are found in the param dict. + layer_names = {x for x in params['params'] if x.startswith('Dense_')} + + self.assertLen(layer_names, num_layers + 1) + + @parameterized.named_parameters( + ('relu', nn.relu), ('sigmoid', nn.sigmoid), ('tanh', nn.tanh) + ) + def test_network_activation_initializer(self, activation): + network = continuous_networks.ActorNetwork( + self.action_shape, activation=activation + ) + self.assertEqual(network.activation, activation) + + @parameterized.named_parameters( + ('glorot_uniform', jax.nn.initializers.glorot_uniform()), + ('glorot_normal', jax.nn.initializers.glorot_normal()), + ('uniform', jax.nn.initializers.uniform()), + ) + def test_network_kernel_initializer(self, kernel_initializer): + network = continuous_networks.ActorNetwork( + self.action_shape, kernel_initializer=kernel_initializer + ) + self.assertEqual(network.kernel_initializer, kernel_initializer) + + +class CriticNetworkTest(parameterized.TestCase): def setUp(self): super().setUp() @@ -29,8 +85,8 @@ def setUp(self): self.example_action = jnp.zeros((3,), dtype=jnp.float32) self.action_shape = (4,) - def test_sac_critic_network_outputs_single_value(self): - network_def = continuous_networks.SACCriticNetwork() + def test_critic_network_outputs_single_value(self): + network_def = continuous_networks.CriticNetwork() params = network_def.init(self.key, self.example_state, self.example_action) output = network_def.apply(params, self.example_state, self.example_action) @@ -39,7 +95,7 @@ def test_sac_critic_network_outputs_single_value(self): def test_network_has_specified_number_of_layers(self): num_layers = 5 - network_def = continuous_networks.SACCriticNetwork(num_layers=num_layers) + network_def = continuous_networks.CriticNetwork(num_layers=num_layers) params = network_def.init(self.key, self.example_state, self.example_action) # Checks which dense layers are found in the param dict. @@ -48,8 +104,26 @@ def test_network_has_specified_number_of_layers(self): # n hidden layers + 1 output layer. self.assertLen(layer_names, num_layers + 1) + @parameterized.named_parameters( + ('relu', nn.relu), ('sigmoid', nn.sigmoid), ('tanh', nn.tanh) + ) + def test_network_activation_initializer(self, activation): + network = continuous_networks.CriticNetwork(activation=activation) + self.assertEqual(network.activation, activation) + + @parameterized.named_parameters( + ('glorot_uniform', jax.nn.initializers.glorot_uniform()), + ('glorot_normal', jax.nn.initializers.glorot_normal()), + ('uniform', jax.nn.initializers.uniform()), + ) + def test_network_kernel_initializer(self, kernel_initializer): + network = continuous_networks.CriticNetwork( + kernel_initializer=kernel_initializer + ) + self.assertEqual(network.kernel_initializer, kernel_initializer) + -class SacNetworkTest(absltest.TestCase): +class ActorCriticNetworkTest(absltest.TestCase): def setUp(self): super().setUp() @@ -60,13 +134,13 @@ def setUp(self): self.example_action = jnp.zeros(self.action_shape, dtype=jnp.float32) self.example_state = jnp.zeros((8,), dtype=jnp.float32) - self.network_def = continuous_networks.SACNetwork( + self.network_def = continuous_networks.ActorCriticNetwork( self.action_shape, self.num_layers ) self.params = self.network_def.init(self.key, self.example_state, self.key) def assert_actor_shapes_are_correct( - self, actor_output: continuous_networks.SacActorOutput + self, actor_output: continuous_networks.ActorOutput ): with self.subTest(name='mean_action'): self.assertEqual(actor_output.mean_action.shape, self.action_shape) @@ -76,29 +150,29 @@ def assert_actor_shapes_are_correct( self.assertEqual(actor_output.log_probability.shape, ()) def assert_critic_shapes_are_correct( - self, critic_output: continuous_networks.SacCriticOutput + self, critic_output: continuous_networks.CriticOutput ): with self.subTest(name='q_value1'): self.assertEqual(critic_output.q_value1.shape, (1,)) with self.subTest(name='q_value2'): self.assertEqual(critic_output.q_value2.shape, (1,)) - def test_sac_network_call_outputs_correct_shaped_values(self): + def test_actor_critic_network_call_outputs_correct_shaped_values(self): output = self.network_def.apply(self.params, self.example_state, self.key) - self.assertIsInstance(output, continuous_networks.SacOutput) + self.assertIsInstance(output, continuous_networks.ActorCriticOutput) self.assert_actor_shapes_are_correct(output.actor) self.assert_critic_shapes_are_correct(output.critic) - def test_sac_network_actor_outputs_correct_shaped_values(self): + def test_actor_critic_network_actor_outputs_correct_shaped_values(self): output = self.network_def.apply( self.params, self.example_state, self.key, method=self.network_def.actor ) - self.assertIsInstance(output, continuous_networks.SacActorOutput) + self.assertIsInstance(output, continuous_networks.ActorOutput) self.assert_actor_shapes_are_correct(output) - def test_sac_network_critic_outputs_correct_shaped_values(self): + def test_actor_critic_network_critic_outputs_correct_shaped_values(self): output = self.network_def.apply( self.params, self.example_state, @@ -106,17 +180,16 @@ def test_sac_network_critic_outputs_correct_shaped_values(self): method=self.network_def.critic, ) - self.assertIsInstance(output, continuous_networks.SacCriticOutput) + self.assertIsInstance(output, continuous_networks.CriticOutput) self.assert_critic_shapes_are_correct(output) def test_network_has_specified_number_of_layers(self): - # Checks which actor layers are found in the param dict. actor_layer_names = { - x for x in self.params['params'] if x.startswith('_actor_layers_') + x for x in self.params['params']['_actor'] if x.startswith('Dense_') } with self.subTest('actor'): - self.assertLen(actor_layer_names, self.num_layers) - self.assertIn('_actor_final_layer', self.params['params']) + # n hidden layers + 1 output layer. + self.assertLen(actor_layer_names, self.num_layers + 1) critic1_layer_names = { x for x in self.params['params']['_critic1'] if x.startswith('Dense_') @@ -132,5 +205,179 @@ def test_network_has_specified_number_of_layers(self): self.assertLen(critic2_layer_names, self.num_layers + 1) +class PPOActorNetworkTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.key = jax.random.PRNGKey(0) + self.example_state = jnp.zeros((8,), dtype=jnp.float32) + self.action_shape = (4,) + + def test_ppo_actor_network_outputs_correct_shaped_values(self): + network_def = continuous_networks.PPOActorNetwork(self.action_shape) + params = network_def.init(self.key, self.example_state, self.key) + + sampled_action, action_probability, entropy = network_def.apply( + params, self.example_state, self.key + ) + + self.assertEqual(sampled_action.shape, self.action_shape) + self.assertEqual(action_probability.shape, ()) + self.assertEqual(entropy.shape, ()) + + def test_ppo_network_has_specified_number_of_layers(self): + num_layers = 5 + network_def = continuous_networks.PPOActorNetwork( + self.action_shape, num_layers + ) + params = network_def.init(self.key, self.example_state, self.key) + + # Checks which dense layers are found in the param dict. + layer_names = {x for x in params['params'] if x.startswith('Dense_')} + + # n hidden layers + 2 output layers. + self.assertLen(layer_names, num_layers + 2) + + @parameterized.named_parameters( + ('relu', nn.relu), ('sigmoid', nn.sigmoid), ('tanh', nn.tanh) + ) + def test_ppo_network_activation_initializer(self, activation): + network = continuous_networks.PPOActorNetwork( + self.action_shape, activation=activation + ) + self.assertEqual(network.activation, activation) + + def test_scale_diag_set_to_zero(self): + num_layers = 4 + hidden_units = 256 + network = continuous_networks.PPOActorNetwork( + self.action_shape, num_layers, hidden_units + ) + params = network.init(self.key, self.example_state, self.key) + np.testing.assert_array_equal( + params['params'][f'Dense_{num_layers+1}']['kernel'], + jnp.zeros((hidden_units,) + self.action_shape), + ) + + +class PPOCriticNetworkTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.key = jax.random.PRNGKey(0) + self.example_state = jnp.zeros((8,), dtype=jnp.float32) + self.action_shape = (4,) + + def test_ppo_critic_network_outputs_single_value(self): + network_def = continuous_networks.PPOCriticNetwork() + params = network_def.init(self.key, self.example_state) + + output = network_def.apply(params, self.example_state) + + self.assertEqual(output.shape, (1,)) + + def test_ppo_network_has_specified_number_of_layers(self): + num_layers = 5 + network_def = continuous_networks.PPOCriticNetwork(num_layers=num_layers) + params = network_def.init(self.key, self.example_state) + + # Checks which dense layers are found in the param dict. + layer_names = {x for x in params['params'] if x.startswith('Dense_')} + + # n hidden layers + 1 output layer. + self.assertLen(layer_names, num_layers + 1) + + @parameterized.named_parameters( + ('relu', nn.relu), ('sigmoid', nn.sigmoid), ('tanh', nn.tanh) + ) + def test_ppo_network_activation_initializer(self, activation): + network = continuous_networks.PPOCriticNetwork(activation=activation) + self.assertEqual(network.activation, activation) + + +class PPOActorCriticNetworkTest(absltest.TestCase): + + def setUp(self): + super().setUp() + self.key = jax.random.PRNGKey(0) + self.action_shape = (3,) + self.num_layers = 3 + + self.example_state = jnp.zeros((8,), dtype=jnp.float32) + + self.network_def = continuous_networks.PPOActorCriticNetwork( + self.action_shape, self.num_layers + ) + self.params = self.network_def.init(self.key, self.example_state, self.key) + + def assert_ppo_actor_shapes_are_correct( + self, actor_output: continuous_networks.PPOActorOutput + ): + with self.subTest(name='sampled_action'): + self.assertEqual(actor_output.sampled_action.shape, self.action_shape) + with self.subTest(name='log_probability'): + self.assertEqual(actor_output.log_probability.shape, ()) + with self.subTest(name='entropy'): + self.assertEqual(actor_output.entropy.shape, ()) + + def assert_ppo_critic_shapes_are_correct( + self, critic_output: continuous_networks.PPOCriticOutput + ): + with self.subTest(name='q_value'): + self.assertEqual(critic_output.q_value.shape, (1,)) + + def test_ppo_actor_critic_network_call_outputs_correct_shaped_values(self): + output = self.network_def.apply(self.params, self.example_state, self.key) + + self.assertIsInstance(output, continuous_networks.PPOActorCriticOutput) + self.assert_ppo_actor_shapes_are_correct(output.actor) + self.assert_ppo_critic_shapes_are_correct(output.critic) + + def test_ppo_actor_critic_network_actor_outputs_correct_shaped_values(self): + output = self.network_def.apply( + self.params, self.example_state, self.key, method=self.network_def.actor + ) + + self.assertIsInstance(output, continuous_networks.PPOActorOutput) + self.assert_ppo_actor_shapes_are_correct(output) + + def test_ppo_actor_critic_network_critic_outputs_correct_shaped_values(self): + output = self.network_def.apply( + self.params, + self.example_state, + method=self.network_def.critic, + ) + + self.assertIsInstance(output, continuous_networks.PPOCriticOutput) + self.assert_ppo_critic_shapes_are_correct(output) + + def test_network_has_specified_number_of_layers(self): + actor_layer_names = { + x for x in self.params['params']['_actor'] if x.startswith('Dense_') + } + with self.subTest('actor'): + # n hidden layers + 2 output layers. + self.assertLen(actor_layer_names, self.num_layers + 2) + + critic_layer_names = { + x for x in self.params['params']['_critic'] if x.startswith('Dense_') + } + with self.subTest('critic'): + # n hidden layers + 1 output layer. + self.assertLen(critic_layer_names, self.num_layers + 1) + + +class ContinousNetworksHelperTest(parameterized.TestCase): + + @parameterized.parameters(('relu', nn.relu), ('tanh', nn.tanh)) + def test_create_activation(self, name, activation): + created_activation = continuous_networks.create_activation(name) + self.assertEqual(created_activation, activation) + + def test_create_activation_raises_error(self): + with self.assertRaises(ValueError): + continuous_networks.create_activation('not_an_activation') + + if __name__ == '__main__': absltest.main() diff --git a/tests/dopamine/jax/networks_test.py b/tests/dopamine/jax/networks_test.py index 2a512514..c4765603 100644 --- a/tests/dopamine/jax/networks_test.py +++ b/tests/dopamine/jax/networks_test.py @@ -77,6 +77,38 @@ def get_q_values(states): batch_q_values.shape, (self._batch_size, self._num_actions) ) + def testPPOOutputShape(self): + network = networks.PPODiscreteActorCriticNetwork( + action_shape=(self._num_actions,) + ) + x = onp.ones(self._input_shape) + params = network.init(self._rng, x, self._rng) + + def get_output(states): + return network.apply(params, states, self._rng) + + output = get_output(x) + onp.testing.assert_equal(output.actor.sampled_action.shape, ()) + onp.testing.assert_equal(output.actor.log_probability.shape, ()) + onp.testing.assert_equal(output.actor.entropy.shape, ()) + onp.testing.assert_equal(output.critic.q_value.shape, (1,)) + + batch_output = jax.vmap(get_output, in_axes=(0,))( + onp.ones(self._batch_input_shape) + ) + onp.testing.assert_equal( + batch_output.actor.sampled_action.shape, (self._batch_size,) + ) + onp.testing.assert_equal( + batch_output.actor.log_probability.shape, (self._batch_size,) + ) + onp.testing.assert_equal( + batch_output.actor.entropy.shape, (self._batch_size,) + ) + onp.testing.assert_equal( + batch_output.critic.q_value.shape, (self._batch_size, 1) + ) + if __name__ == '__main__': absltest.main() diff --git a/tests/dopamine/jax/replay_memory/accumulator_test.py b/tests/dopamine/jax/replay_memory/accumulator_test.py new file mode 100644 index 00000000..f92110f5 --- /dev/null +++ b/tests/dopamine/jax/replay_memory/accumulator_test.py @@ -0,0 +1,207 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for accumulator.py.""" + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import elements +import numpy as np + +mock = absltest.mock + + +class TransitionAccumulatorTest(parameterized.TestCase): + + def _verify_accumulator_transition( + self, + accumulator_transition: elements.TransitionElement, + transition: elements.TransitionElement | None = None, + ): + transition = self._sample_transition if transition is None else transition + # The observation is removed from elements_accumulator since it is stored in + # the stack. + np.testing.assert_array_equal( + accumulator_transition.observation, transition.observation + ) + self.assertEqual(accumulator_transition.action, transition.action) + self.assertEqual(accumulator_transition.reward, transition.reward) + self.assertEqual( + accumulator_transition.is_terminal, int(transition.is_terminal) + ) + self.assertEqual( + accumulator_transition.episode_end, int(transition.episode_end) + ) + + def _add( + self, + acc: accumulator.TransitionAccumulator, + transition: elements.TransitionElement, + ): + return list(acc.accumulate(transition)) + + def setUp(self): + super().setUp() + self._stack_size = 4 + self._update_horizon = 3 + self._gamma = 0.9 + self._obs = np.ones((4, 3)) + self._sample_transition = elements.TransitionElement( + self._obs, 1, 3.14, False, False + ) + + def testInitializer(self): + acc = accumulator.TransitionAccumulator( + self._stack_size, self._update_horizon, self._gamma + ) + self.assertEmpty(acc._trajectory) + + def testReset(self): + acc = accumulator.TransitionAccumulator( + self._stack_size, self._update_horizon, self._gamma + ) + # We add enough transitions for a valid trajectory. + for i in range(self._update_horizon + self._stack_size + 1): + self._add( + acc, elements.TransitionElement(self._obs * i, i, i, False, False) + ) + self.assertLen(acc._trajectory, self._stack_size + self._update_horizon) + self._add(acc, elements.TransitionElement(self._obs * 8, 8, 8, True, True)) + self.assertEmpty(acc._trajectory) + + def testOneElementTrajectoriesAreInvalid(self): + acc = accumulator.TransitionAccumulator( + self._stack_size, self._update_horizon, self._gamma + ) + timesteps = self._add(acc, self._sample_transition) + self.assertEmpty(timesteps) + self._verify_accumulator_transition(acc._trajectory[0]) + # The trajectory accumulator shouldn't have been cleared + self.assertNotEmpty(acc._trajectory) + + @parameterized.named_parameters( + dict(testcase_name='OneStep', n=1, valid=True), + dict(testcase_name='MultiStep', n=3, valid=False), + dict(testcase_name='MultiStepGreaterThanStack', n=5, valid=False), + ) + def testAddSingleTrajectory(self, n: int, valid: bool): + acc = accumulator.TransitionAccumulator(self._stack_size, n, self._gamma) + # We need at least two transitions per trajectory, otherwise there is no + # next state. + self.assertListEqual(self._add(acc, self._sample_transition), []) + timesteps = self._add(acc, self._sample_transition) + # Verify the stack now has elements. + self.assertLen(acc._trajectory, 2) + + self._verify_accumulator_transition(acc._trajectory[0]) + self._verify_accumulator_transition(acc._trajectory[0]) + + if not valid: + self.assertEmpty(timesteps) + else: + self.assertLen(timesteps, 1) + self.assertIsNotNone(timesteps[0]) + # Two last elements of the stack should be the observations just added. + np.testing.assert_array_equal(timesteps[0].state[..., -1], self._obs) + np.testing.assert_allclose(timesteps[0].state[..., :-1], 0) + + np.testing.assert_array_equal(timesteps[0].next_state[..., -1], self._obs) + np.testing.assert_array_equal(timesteps[0].next_state[..., -2], self._obs) + np.testing.assert_allclose(timesteps[0].next_state[..., :-2], 0) + + self.assertEqual(timesteps[0].action, acc._trajectory[0].action) + self.assertEqual(timesteps[0].is_terminal, acc._trajectory[1].is_terminal) + self.assertEqual(timesteps[0].episode_end, acc._trajectory[1].episode_end) + + self.assertEqual( + timesteps[0].reward, + np.sum( + self._sample_transition.reward * (self._gamma ** np.arange(n)) + ), + ) + + @parameterized.parameters(4, 5, 6) + def testAccumulate(self, n): + acc = accumulator.TransitionAccumulator(self._stack_size, n, self._gamma) + # We add enough transitions for a valid trajectory. + for i in range(8): + element = list( + acc.accumulate( + elements.TransitionElement(self._obs * i, i, i, False, False) + ) + ) + if i > n - 1: + self.assertLen(element, 1) + self.assertIsNotNone(element[0]) + else: + self.assertEmpty(element) + + def testAccumulateWithInvalidFirstTrajectory(self): + acc = accumulator.TransitionAccumulator(self._stack_size, 1, self._gamma) + # We add a single terminal trajectory which is invalid, and should thus + # return None. + element = list( + acc.accumulate(elements.TransitionElement(self._obs, 1, 1, True, False)) + ) + self.assertEmpty(element) + # Adding a new transition should still return None, as it is not yet valid. + # This will also have the hidden effect of removing the first (invalid) + # trajectory. + element = list( + acc.accumulate( + elements.TransitionElement(self._obs * 2, 2, 2, False, False) + ) + ) + self.assertEmpty(element) + # Now adding a third transition (the second of the second trajectory) will + # result in a valid element returned. This element will be the second + # transition added (or the first in the second trajectory). + element = list( + acc.accumulate( + elements.TransitionElement(self._obs * 3, 3, 3, False, False) + ) + ) + self.assertLen(element, 1) + self.assertIsNotNone(element[0]) + # The state returned will be a stack with the first observation at + # the end. + state = [np.zeros_like(self._obs) for _ in range(self._stack_size)] + state[-1] = self._obs * 2 + state = np.moveaxis(state, 0, -1) + # The next_state returned will be a stack with the last two observations at + # the end. + next_state = [np.zeros_like(self._obs) for _ in range(self._stack_size)] + next_state[-2] = self._obs * 2 + next_state[-1] = self._obs * 3 + next_state = np.moveaxis(next_state, 0, -1) + np.testing.assert_array_equal(state, element[0].state) + np.testing.assert_array_equal(next_state, element[0].next_state) + self.assertEqual(2, element[0].action) + self.assertEqual(2.0, element[0].reward) + self.assertFalse(element[0].is_terminal) + self.assertFalse(element[0].episode_end) + + def testClear(self): + acc = accumulator.TransitionAccumulator( + self._stack_size, self._update_horizon, self._gamma + ) + self._add(acc, self._sample_transition) + self.assertNotEmpty(acc._trajectory) + acc.clear() + self.assertEmpty(acc._trajectory) + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/jax/replay_memory/elements_test.py b/tests/dopamine/jax/replay_memory/elements_test.py new file mode 100644 index 00000000..494b7fac --- /dev/null +++ b/tests/dopamine/jax/replay_memory/elements_test.py @@ -0,0 +1,62 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for replay element data structure.""" + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax.replay_memory import elements +import numpy as np + + +mock = absltest.mock + + +class ElementsTest(parameterized.TestCase): + + def test_pack_unpack(self) -> None: + """Simple test case that packs and unpacks a replay element.""" + state = np.zeros((84, 84, 4), dtype=np.uint8) + next_state = np.ones((84, 84, 4), dtype=np.uint8) + action = 1 + reward = 1.0 + episode_end = False + + element = elements.ReplayElement( + state=state, + action=action, + reward=reward, + next_state=next_state, + is_terminal=episode_end, + episode_end=episode_end, + ) + + packed = element.pack() + assert packed.is_compressed + assert packed.action == action + assert packed.reward == reward + assert packed.is_terminal == packed.episode_end == episode_end + + unpacked = packed.unpack() + assert not unpacked.is_compressed + assert unpacked.action == action + assert unpacked.reward == reward + assert unpacked.is_terminal == unpacked.episode_end == episode_end + + np.testing.assert_array_equal(unpacked.state, state) + np.testing.assert_array_equal(unpacked.next_state, next_state) + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/jax/replay_memory/replay_buffer_regression_test.py b/tests/dopamine/jax/replay_memory/replay_buffer_regression_test.py new file mode 100644 index 00000000..4c88f48f --- /dev/null +++ b/tests/dopamine/jax/replay_memory/replay_buffer_regression_test.py @@ -0,0 +1,240 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Regression tests for replay_buffer.py. + +These tests are based on the tests for the original circular replay buffer, and +are meant to ensure the new buffer behaves as expected. +""" + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers +import numpy as np + + +# Default parameters used when creating the replay memory. +OBSERVATION_SHAPE = (84, 84) +OBS_DTYPE = np.uint8 +STACK_SIZE = 4 +BATCH_SIZE = 32 + + +class ReplayBufferRegressionTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + num_dims = 10 + self._test_observation = np.ones(num_dims) * 1 + self._test_action = np.ones(num_dims) * 2 + self._test_reward = np.ones(num_dims) * 3 + self._test_terminal = np.ones(num_dims) * 4 + self._test_add_count = np.array(7) + self._transition_accumulator = accumulator.TransitionAccumulator( + stack_size=STACK_SIZE, + update_horizon=5, + gamma=1.0, + ) + self._sampling_distribution = samplers.UniformSamplingDistribution(seed=0) + + def testNSteprewards(self): + memory = replay_buffer.ReplayBuffer( + transition_accumulator=self._transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=BATCH_SIZE, + max_capacity=10, + compress=False, + ) + + for i in range(50): + transition = elements.TransitionElement( + np.full(OBSERVATION_SHAPE, i, dtype=OBS_DTYPE), 0, 2.0, False + ) + memory.add(transition) + + for _ in range(100): + batch = memory.sample() + # Make sure the total reward is reward per step x update_horizon. + np.testing.assert_array_equal(batch.reward, np.ones(BATCH_SIZE) * 10.0) + + def testGetStack(self): + zero_state = np.zeros(OBSERVATION_SHAPE + (3,), dtype=OBS_DTYPE) + + memory = replay_buffer.ReplayBuffer( + transition_accumulator=self._transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=BATCH_SIZE, + max_capacity=50, + compress=False, + ) + for i in range(11): + transition = elements.TransitionElement( + np.full(OBSERVATION_SHAPE, i, dtype=OBS_DTYPE), 0, 0, False + ) + memory.add(transition) + + # ensure that the returned shapes are always correct + for i in memory._memory: + np.testing.assert_array_equal( + memory._memory[i].state.shape, OBSERVATION_SHAPE + (4,) + ) + + # ensure that there is the necessary 0 padding + state = memory._memory[0].state + np.testing.assert_array_equal(zero_state, state[:, :, :3]) + + # ensure that after the padding the contents are properly stored + state = memory._memory[3].state + for i in range(4): + np.testing.assert_array_equal( + np.full(OBSERVATION_SHAPE, i), state[:, :, i] + ) + + def testSampleTransitionBatch(self): + replay_capacity = 10 + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=1, + update_horizon=1, + gamma=0.99, + ) + memory = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=2, + max_capacity=replay_capacity, + ) + num_adds = 50 # The number of transitions to add to the memory. + # Since terminal transitions are not valid trajectories (since there is no + # next state), they will not be sent back to the replay buffer memory. + # Thus, we do not have a one-to-one correspondence between indices and the + # value contained in the respective state. We use this map to keep track of + # the mapping from indices to sample and state value. + index_to_id = [] + for i in range(num_adds): + terminal = i % 4 == 0 # Every 4 transitions is terminal. + transition = elements.TransitionElement( + np.full(OBSERVATION_SHAPE, i, OBS_DTYPE), 0, 0, terminal, False + ) + memory.add(transition) + if not terminal: + index_to_id.append(i) + # Test sampling with default batch size. + for _ in range(1000): + batch = memory.sample() + self.assertEqual(batch.state.shape[0], 2) + # Test changing batch sizes. + for _ in range(1000): + batch = memory.sample(BATCH_SIZE) + self.assertEqual(batch.state.shape[0], BATCH_SIZE) + # Verify we revert to default batch size. + for _ in range(1000): + batch = memory.sample() + self.assertEqual(batch.state.shape[0], 2) + + sampler = memory._sampling_distribution + self.assertIsInstance(sampler, samplers.UniformSamplingDistribution) + + # Verify we sample the expected indices. + # Use the same rng state for reprodcibility. + rng = np.random.default_rng(0) + rng.bit_generator.state = sampler._rng.bit_generator.state + indices = rng.integers( + len(sampler._key_by_index), size=len(sampler._key_by_index) + ) + + def make_state(key: int) -> np.ndarray: + return np.full(OBSERVATION_SHAPE + (1,), key, dtype=OBS_DTYPE) + + expected_states = np.array( + [make_state(index_to_id[sampler._key_by_index[i]]) for i in indices] + ) + expected_next_states = np.array( + [make_state(index_to_id[sampler._key_by_index[i]] + 1) for i in indices] + ) + # This is replicating the formula that was used above to determine what + # transitions are terminal when adding observation (i % 4). However, we add + # 1 to the index, as it is the _next_ state that determines whether this was + # a terminal transition or not. + expected_terminal = np.array([ + int(((index_to_id[sampler._key_by_index[i]] + 1) % 4) == 0) + for i in indices + ]) + batch = memory.sample(size=len(indices)) + np.testing.assert_array_equal(batch.state, expected_states) + np.testing.assert_array_equal(batch.action, np.zeros(len(indices))) + np.testing.assert_array_equal(batch.reward, np.zeros(len(indices))) + np.testing.assert_array_equal(batch.next_state, expected_next_states) + np.testing.assert_array_equal(batch.is_terminal, expected_terminal) + + def testSamplingWithTerminalInTrajectory(self): + replay_capacity = 10 + update_horizon = 3 + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=1, + update_horizon=update_horizon, + gamma=1.0, + ) + memory = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=2, + max_capacity=replay_capacity, + ) + for i in range(replay_capacity): + transition = elements.TransitionElement( + np.full(OBSERVATION_SHAPE, i, OBS_DTYPE), + i * 2, # action + i, # reward + i == 3, # terminal + False, # episode_end + ) + memory.add(transition) + # Verify we sample the expected indices, using the same rng. + rng = np.random.default_rng(0) + indices = rng.integers(memory.add_count, size=(5,)) + batch = memory.sample(size=5) + # Since index 3 is terminal, it will not be a valid transition so we need to + # "renumber" accordingly. + expected_states = np.array([ + np.full(OBSERVATION_SHAPE + (1,), i, dtype=OBS_DTYPE) + if i < 3 + else np.full(OBSERVATION_SHAPE + (1,), i + 1, dtype=OBS_DTYPE) + for i in indices + ]) + expected_actions = np.array( + [i * 2 if i < 3 else (i + 1) * 2 for i in indices] + ) + # The reward in the replay buffer will be (an asterisk marks the terminal + # state): + # [0 1 2 3* 4 5 6 7 8 9] + # Since we're setting the update_horizon to 3, the accumulated trajectory + # reward starting at each of the replay buffer positions will be (a '_' + # marks an invalid transition to sample): + # [3 6 5 _ 15 18 21 24] + expected_rewards = np.array([3, 6, 5, 15, 18, 21, 24]) + # Because update_horizon = 3, indices 0, 1 and 2 include terminal. + expected_terminals = np.array([1, 1, 1, 0, 0, 0, 0]) + np.testing.assert_array_equal(batch.state, expected_states) + np.testing.assert_array_equal(batch.action, expected_actions) + np.testing.assert_array_equal(batch.reward, expected_rewards[indices]) + np.testing.assert_array_equal( + batch.is_terminal, expected_terminals[indices] + ) + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/jax/replay_memory/replay_buffer_test.py b/tests/dopamine/jax/replay_memory/replay_buffer_test.py new file mode 100644 index 00000000..89fd2ba4 --- /dev/null +++ b/tests/dopamine/jax/replay_memory/replay_buffer_test.py @@ -0,0 +1,527 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for replay_buffer.py. + +The actual replay buffer doesn't have much surface area as it depends on +a sampler and accumulator and these are both tested independently. +We only test the legacy replay buffer here which validates the integration +of the sampler and accumulator. +""" + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax.replay_memory import accumulator +from dopamine.jax.replay_memory import elements +from dopamine.jax.replay_memory import replay_buffer +from dopamine.jax.replay_memory import samplers +from etils import epath +import msgpack +import numpy as np + +mock = absltest.mock + + +class ReplayBufferTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self._tmpdir = epath.Path(self.create_tempdir('checkpoint').full_path) + self._obs = np.ones((4, 3)) + self._transition_accumulator = accumulator.TransitionAccumulator( + stack_size=2, + update_horizon=10, + gamma=0.9, + ) + self._sampling_distribution = samplers.UniformSamplingDistribution(seed=0) + self._sample_transition = elements.TransitionElement( + self._obs, 1, 3.14, False, False + ) + + @parameterized.parameters((-1,), (0,)) + def testWithInvalidCheckpointDuration(self, cd): + with self.assertRaises(ValueError): + _ = replay_buffer.ReplayBuffer( + transition_accumulator=self._transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=32, + max_capacity=100, + checkpoint_duration=cd, + ) + + def testCreateReplayBuffer(self): + _ = replay_buffer.ReplayBuffer( + transition_accumulator=self._transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=32, + max_capacity=100, + compress=True, + ) + + @mock.patch.object(accumulator, 'TransitionAccumulator', autospec=True) + def testAddWithEmptyAccumulatorReturn(self, mock_accumulator): + instance = mock_accumulator.return_value + instance.accumulate.return_value = [] + replay = replay_buffer.ReplayBuffer( + transition_accumulator=instance, + sampling_distribution=self._sampling_distribution, + batch_size=32, + max_capacity=100, + ) + replay.add(self._sample_transition) + instance.accumulate.assert_called_with(self._sample_transition) + self.assertEmpty(replay._memory) + + @mock.patch.object(accumulator, 'TransitionAccumulator', autospec=True) + def testAddWithoutCompress(self, mock_accumulator): + instance = mock_accumulator.return_value + instance.accumulate.return_value = [] + replay = replay_buffer.ReplayBuffer( + transition_accumulator=instance, + sampling_distribution=self._sampling_distribution, + batch_size=32, + max_capacity=100, + compress=False, + ) + replay.add(self._sample_transition) + instance.accumulate.assert_called_with(self._sample_transition) + self.assertEmpty(replay._memory) + + @mock.patch.object(accumulator, 'TransitionAccumulator', autospec=True) + def testAddWithValidAccumulatorReturn(self, mock_accumulator): + instance = mock_accumulator.return_value + instance.accumulate.return_value = [self._sample_transition] + replay = replay_buffer.ReplayBuffer( + transition_accumulator=instance, + sampling_distribution=self._sampling_distribution, + batch_size=32, + max_capacity=100, + compress=False, + ) + replay.add(self._sample_transition) + self.assertLen(replay._memory, 1) + self.assertEqual(list(replay._memory.keys()), [0]) + np.testing.assert_array_equal( + replay._memory[0].observation, self._sample_transition.observation + ) + self.assertEqual(replay._memory[0].action, self._sample_transition.action) + self.assertEqual(replay._memory[0].reward, self._sample_transition.reward) + self.assertEqual( + replay._memory[0].is_terminal, int(self._sample_transition.is_terminal) + ) + self.assertEqual( + replay._memory[0].episode_end, int(self._sample_transition.episode_end) + ) + + @mock.patch.object(accumulator, 'TransitionAccumulator', autospec=True) + def testAddUpToCapacity(self, mock_accumulator): + instance = mock_accumulator.return_value + capacity = 10 + replay = replay_buffer.ReplayBuffer( + transition_accumulator=instance, + sampling_distribution=self._sampling_distribution, + batch_size=32, + max_capacity=capacity, + compress=False, + ) + transitions = [] + for i in range(15): + transitions.append( + elements.TransitionElement(self._obs * i, i, i, False, False) + ) + instance.accumulate.return_value = [transitions[-1]] + replay.add(transitions[-1]) + # Since we created the ReplayBuffer with a capacity of 10, it should have + # gotten rid of the first 5 elements added. + self.assertLen(replay._memory, capacity) + expected_keys = list(range(5, 5 + capacity)) + self.assertEqual(list(replay._memory.keys()), expected_keys) + for i in expected_keys: + np.testing.assert_array_equal( + replay._memory[i].observation, transitions[i].observation + ) + self.assertEqual(replay._memory[i].action, transitions[i].action) + self.assertEqual(replay._memory[i].reward, transitions[i].reward) + self.assertEqual( + replay._memory[i].is_terminal, int(transitions[i].is_terminal) + ) + self.assertEqual( + replay._memory[i].episode_end, int(transitions[i].episode_end) + ) + + def testSampleWithNoElements(self): + replay = replay_buffer.ReplayBuffer( + transition_accumulator=self._transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=32, + max_capacity=100, + ) + with self.assertRaises(ValueError): + replay.sample() + with self.assertRaises(ValueError): + replay.sample(512) + + @parameterized.named_parameters( + dict( + testcase_name='DefaultBSNoCompress', + use_default_bs=True, + compress=False, + ), + dict( + testcase_name='NoDefaultBSNoCompress', + use_default_bs=False, + compress=False, + ), + dict( + testcase_name='DefaultBSCompress', use_default_bs=True, compress=True + ), + dict( + testcase_name='NoDefaultBSCompress', + use_default_bs=False, + compress=True, + ), + ) + @mock.patch.object(accumulator, 'TransitionAccumulator', autospec=True) + def testSample(self, mock_accumulator, use_default_bs, compress): + instance = mock_accumulator.return_value + seed = 0 + capacity = 100 + default_batch_size = 32 + replay = replay_buffer.ReplayBuffer( + transition_accumulator=instance, + sampling_distribution=self._sampling_distribution, + batch_size=default_batch_size, + max_capacity=capacity, + compress=compress, + ) + transitions = [] + for i in range(100): + transitions.append( + elements.TransitionElement(self._obs * i, i, i, False, False) + ) + state = transitions[-1].observation + next_state = self._obs * (i + 1) + instance.accumulate.return_value = [ + elements.ReplayElement( + state=state, + next_state=next_state, + action=i, # action + reward=i, # reward + is_terminal=int(transitions[-1].is_terminal), + episode_end=int(transitions[-1].episode_end), + ) + ] + replay.add(transitions[-1]) + expected_keys = list(range(100)) + self.assertEqual(list(replay._memory.keys()), expected_keys) + batch_size = default_batch_size if use_default_bs else 16 + rng = np.random.default_rng(seed) # Use the same numpy RNG. + if use_default_bs: + batches = replay.sample() + else: + batches = replay.sample(size=batch_size) + self.assertEqual(batches.state.shape[0], batch_size) + self.assertEqual(batches.next_state.shape[0], batch_size) + self.assertEqual(batches.action.shape[0], batch_size) + self.assertEqual(batches.reward.shape[0], batch_size) + self.assertLen(batches.is_terminal, batch_size) + self.assertLen(batches.episode_end, batch_size) + + # Reset numpy random state. + indices = rng.integers(100, size=(batch_size,)) + for i, idx in enumerate(indices): + np.testing.assert_array_equal( + batches.state[i], transitions[idx].observation + ) + np.testing.assert_array_equal( + batches.next_state[i], np.ones_like(self._obs) * (idx + 1) + ) + self.assertEqual(batches.action[i], idx) + self.assertEqual(batches.reward[i], idx) + self.assertEqual(batches.is_terminal[i], 0) + self.assertEqual(batches.episode_end[i], 0) + + @parameterized.parameters((True,), (False,)) + def testSave(self, compress): + stack_size = 4 + replay_capacity = 50 + batch_size = 32 + update_horizon = 3 + gamma = 0.9 + checkpoint_duration = 7 + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=stack_size, + update_horizon=update_horizon, + gamma=gamma, + ) + replay = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=batch_size, + max_capacity=replay_capacity, + checkpoint_duration=checkpoint_duration, + compress=compress, + ) + # Store a few transitions in memory. Since update_horizon is 3, only + # num_adds - 3 elements will actually be in the replay buffer memory. + transitions = [] + num_adds = 15 + for i in range(num_adds): + transitions.append( + elements.TransitionElement(self._obs * i, i, i, False, False) + ) + replay.add(transitions[-1]) + + replay.save(self._tmpdir, 1) + path = self._tmpdir / '1' / 'replay' / 'checkpoint.msgpack' + self.assertTrue(path.exists()) + replay_pack = msgpack.unpackb( + path.read_bytes(), + raw=False, + strict_map_key=False, + ) + self.assertEqual(num_adds - update_horizon, replay_pack['add_count']) + + @parameterized.parameters((1,), (3,), (5,)) + def testGarbageCollection(self, cd): + replay = replay_buffer.ReplayBuffer( + transition_accumulator=self._transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=8, + max_capacity=10, + checkpoint_duration=cd, + ) + num_adds = 20 + # Store transitions and call save each time. + for i in range(num_adds): + replay.add(elements.TransitionElement(self._obs * i, i, i, False, False)) + replay.save(self._tmpdir, i) + for i in range(num_adds): + path = self._tmpdir / f'{i}' / 'replay' + if i < num_adds - cd: + self.assertFalse(path.exists()) + else: + self.assertTrue(path.exists()) + + def testLoad(self): + update_horizon = 3 + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=4, + update_horizon=update_horizon, + gamma=0.99, + ) + replay = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=8, + max_capacity=10, + checkpoint_duration=10, + compress=False, + ) + num_adds = 20 + # Store transitions and call save each time. + for i in range(num_adds): + replay.add(elements.TransitionElement(self._obs * i, i, i, False, False)) + replay.save(self._tmpdir, i) + # add_count should be equal to num_adds - update_horizon now. + self.assertEqual(num_adds - update_horizon, replay.add_count) + # Load a checkpoint from 5 iterations ago (it is zero-indexed so we + # substract 6). + replay.load(self._tmpdir, num_adds - 6) + # add_count should now be equal to num_adds - update_horizon - 5. + self.assertEqual(num_adds - update_horizon - 5, replay.add_count) + + def testSaveAndLoad(self): + # This is similar to testSample, but saves and loads after every addition. + # The resulting behaviour should be identical. + capacity = 100 + batch_size = 32 + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=2, + update_horizon=1, + gamma=0.99, + ) + replay = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=batch_size, + max_capacity=capacity, + ) + transitions = [] + for i in range(100): + transitions.append( + elements.TransitionElement(self._obs * i, i, i, False, False) + ) + replay.add(transitions[-1]) + # The first call to add() will not actually produce anything in the + # memory, as there are not yet any valid transitions. + if i > 0: + _ = replay.sample() + replay.save(self._tmpdir, i) + # We reinitialize the memory to ensure it's loading properly. + replay = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=batch_size, + max_capacity=capacity, + ) + replay.load(self._tmpdir, i) + expected_keys = list(range(99)) # The last transition is invalid. + self.assertEqual(list(replay._memory.keys()), expected_keys) + # Save the rng state before sampling for reprodcibility. + rng_state = replay._sampling_distribution._rng.bit_generator.state + batches = replay.sample() + self.assertEqual(batches.state.shape[0], batch_size) + self.assertEqual(batches.next_state.shape[0], batch_size) + self.assertEqual(batches.action.shape[0], batch_size) + self.assertEqual(batches.reward.shape[0], batch_size) + self.assertLen(batches.is_terminal, batch_size) + self.assertLen(batches.episode_end, batch_size) + + rng = np.random.default_rng(0) + # Set the state to that of the replay before sampling. + rng.bit_generator.state = rng_state + indices = rng.choice(99, size=(batch_size,)) + for i, idx in enumerate(indices): + # We need to stack the observations for proper comparison. + if idx == 0: + stacked_state = [np.zeros_like(self._obs), transitions[idx].observation] + else: + stacked_state = [ + transitions[idx - 1].observation, + transitions[idx].observation, + ] + stacked_next_state = [ + transitions[idx].observation, + transitions[idx + 1].observation, + ] + stacked_state = np.moveaxis(stacked_state, 0, -1) + stacked_next_state = np.moveaxis(stacked_next_state, 0, -1) + np.testing.assert_array_equal(batches.state[i], stacked_state) + np.testing.assert_array_equal(batches.next_state[i], stacked_next_state) + self.assertEqual(batches.action[i], idx) + self.assertEqual(batches.reward[i], idx) + self.assertEqual(batches.is_terminal[i], 0) + self.assertEqual(batches.episode_end[i], 0) + + def testKeyMappingsForSampling(self): + capacity = 10 + batch_size = 32 + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=1, + update_horizon=1, + gamma=0.99, + ) + replay = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=batch_size, + max_capacity=capacity, + ) + sampler = replay._sampling_distribution + + for i in range(capacity + 1): + replay.add(elements.TransitionElement(self._obs * i, i, i, False, False)) + + # While we haven't overwritten any elements we should have + # global indices as being equivalent to local indices + for i in range(capacity): + self.assertIn(i, sampler._index_by_key) + index = sampler._index_by_key[i] + self.assertEqual(i, index) + self.assertEqual(i, sampler._key_by_index[index]) + + # The next key to be inserted will be `capacity` as when we add + # `capacity + 1` the accumulator will insert: (capacity, capacity + 1) + next_key = capacity + replay.add( + elements.TransitionElement( + self._obs * (next_key + 1), + next_key + 1, + next_key + 1, + False, + False, + ) + ) + # We should have deleted the earliest index + self.assertNotIn(0, sampler._index_by_key) + # The local index corresponding to the previous key should have been swapped + self.assertNotEqual(sampler._key_by_index[0], 0) + # We should have inserted the new key into key -> index + self.assertIn(next_key, sampler._index_by_key) + # index -> key should be consistent + self.assertEqual( + next_key, sampler._key_by_index[sampler._index_by_key[next_key]] + ) + + # Sampling + rng = np.random.default_rng(0) + # Set the state to that of the replay before sampling. + rng.bit_generator.state = sampler._rng.bit_generator.state + # Sample local indices + indices = rng.integers(len(sampler._key_by_index), size=(batch_size,)) + # Convert local indices to global keys + keys = (sampler._key_by_index[index] for index in indices) + + # Fetch actual samples from the replay buffer so we can compare + # the global indices + samples = replay.sample() + + # Each index in our samples should have observations that are equal to + # their global key, we can check this: + for i, key in enumerate(keys): + np.testing.assert_array_equal( + samples.state[i, ...], + self._obs[..., None] * key, + ) + np.testing.assert_array_equal( + samples.next_state[i, ...], + self._obs[..., None] * (key + 1), + ) + self.assertEqual(samples.action[i], key) + self.assertEqual(samples.reward[i], key) + self.assertEqual(samples.is_terminal[i], 0) + self.assertEqual(samples.episode_end[i], 0) + + def testClearBuffer(self): + capacity = 10 + batch_size = 32 + transition_accumulator = accumulator.TransitionAccumulator( + stack_size=1, + update_horizon=1, + gamma=0.99, + ) + replay = replay_buffer.ReplayBuffer( + transition_accumulator=transition_accumulator, + sampling_distribution=self._sampling_distribution, + batch_size=batch_size, + max_capacity=capacity, + ) + for i in range(capacity): + replay.add(elements.TransitionElement(self._obs * i, i, i, False, False)) + self.assertNotEmpty(replay._memory) + self.assertNotEqual(replay.add_count, 0) + self.assertNotEmpty(replay._transition_accumulator._trajectory) + self.assertNotEmpty(replay._sampling_distribution._index_by_key) + self.assertNotEmpty(replay._sampling_distribution._key_by_index) + replay.clear() + self.assertEmpty(replay._memory) + self.assertEqual(replay.add_count, 0) + self.assertEmpty(replay._transition_accumulator._trajectory) + self.assertEmpty(replay._sampling_distribution._index_by_key) + self.assertEmpty(replay._sampling_distribution._key_by_index) + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/jax/replay_memory/samplers_test.py b/tests/dopamine/jax/replay_memory/samplers_test.py new file mode 100644 index 00000000..7b065d5d --- /dev/null +++ b/tests/dopamine/jax/replay_memory/samplers_test.py @@ -0,0 +1,244 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Testing samplers.""" + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax.replay_memory import samplers +import numpy as np + + +class UniformSamplingTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.sampler = samplers.UniformSamplingDistribution(0) + + def test_update_does_not_raise_and_logs(self): + with self.assertLogs(level="INFO") as logs: + self.sampler.update(0, None, [], arg_x=1, arg_y=2) + self.assertLen(logs.output, 1) + self.assertContainsSubsequence(logs.output[0], "is a no-op") + + def test_additional_kwargs_to_add_logs(self): + with self.assertLogs(level="INFO") as logs: + self.sampler.add(1, dummy_kwargs="dummy") + self.assertLen(logs.output, 1) + self.assertContainsSubsequence(logs.output[0], "dummy_kwargs") + + def test_sample_when_empty(self): + with self.assertRaises(ValueError): + self.sampler.sample(1) + + def test_removal_of_invalid_key_raises(self): + with self.assertRaises(ValueError): + self.sampler.remove(1) + + @parameterized.parameters((0,), (-1,)) + def test_invalid_sample_size_raises(self, size: int): + with self.assertRaises(ValueError): + self.sampler.sample(size) + + def test_add_and_remove(self): + self.sampler.add(1) + self.sampler.remove(1) + + def test_sample(self): + self.sampler.add(1) + sample = self.sampler.sample(1) + self.assertIsInstance(sample, samplers.SampleMetadata) + self.assertLen(sample.keys, 1) + self.assertEqual(sample.keys[0], 1) + + def test_serializes(self): + sampler = samplers.UniformSamplingDistribution(0) + sampler.add(1) + state_dict = sampler.to_state_dict() + self.assertIn("key_by_index", state_dict) + self.assertIn("index_by_key", state_dict) + self.assertIn("rng_state", state_dict) + + sampler = samplers.UniformSamplingDistribution(0) + sampler.from_state_dict(state_dict) + self.assertEqual(sampler.sample(1).keys, 1) + + def test_clear_sampler(self): + self.sampler.add(1) + self.assertNotEmpty(self.sampler._key_by_index) + self.assertNotEmpty(self.sampler._index_by_key) + self.sampler.clear() + self.assertEmpty(self.sampler._key_by_index) + self.assertEmpty(self.sampler._index_by_key) + + +class PrioritizedSamplingTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.sampler = samplers.PrioritizedSamplingDistribution( + seed=0, max_capacity=10 + ) + + def test_priorities_can_be_updated(self): + keys = [0, 1, 2, 3, 4] + priorities = [1.0, 1.0, 1.0, 1.0, 1.0] + + for key, priority in zip(keys, priorities): + self.sampler.add(key, priority=priority) + + generator_state = self.sampler._rng.bit_generator.state + self.sampler._rng.bit_generator.state = generator_state + sample1 = self.sampler.sample(5) + + priorities = [0.1, 0.9, 5.0, 0.25, 1.0] + for key, priority in zip(keys, priorities): + self.sampler.add(key, priority=priority) + + self.sampler._rng.bit_generator.state = generator_state + sample2 = self.sampler.sample(5) + + with self.assertRaises(AssertionError): + np.testing.assert_array_equal(sample1.keys, sample2.keys) + + def test_priorities_can_be_removed(self): + keys = [0, 1, 2, 3, 4] + priorities = [1.0, 2.0, 3.0, 4.0, 5.0] + + for key, priority in zip(keys, priorities): + self.sampler.add(key, priority=priority) + + self.sampler.sample(5) + for key in keys: + self.sampler.remove(key) + + with self.assertRaises(ValueError): + self.sampler.sample(5) + + def test_zero_priorities_is_uniform_sampling(self): + for index in range(3): + self.sampler.add(index, priority=0.0) + + samples = self.sampler.sample(3) + np.testing.assert_allclose(samples.probabilities, 1 / 3) + + def test_positive_priorities_computes_probabilities(self): + for index in range(3): + self.sampler.add(index, priority=1.0) + + samples = self.sampler.sample(3) + np.testing.assert_allclose(samples.probabilities, 1 / 3) + + @parameterized.parameters((0,), (1,), (2,)) + def test_removal_wont_sample_removed_index(self, index_to_remove: int): + for index in range(3): + self.sampler.add(index, priority=1.0) + + self.sampler.remove(index_to_remove) + samples = self.sampler.sample(1000) + self.assertNoCommonElements(samples.keys, [index_to_remove]) + + def test_clear_sampler(self): + for index in range(3): + self.sampler.add(index, priority=1.0) + self.assertNotEmpty(self.sampler._key_by_index) + self.assertNotEmpty(self.sampler._index_by_key) + self.sampler.clear() + self.assertEmpty(self.sampler._key_by_index) + self.assertEmpty(self.sampler._index_by_key) + self.assertEqual(self.sampler._sum_tree.root, 0.0) + for index in range(3): + self.assertEqual(self.sampler._sum_tree.get(index), 0.0) + + +class SequentialSamplingTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.sampler = samplers.SequentialSamplingDistribution(seed=0) + + def test_sample_when_empty(self): + with self.assertRaises(ValueError): + self.sampler.sample(1) + + def test_sample_when_not_empty(self): + keys = [1, 2, 3] + for key in keys: + self.sampler.add(key) + sample = self.sampler.sample(3) + self.assertIsInstance(sample, samplers.SampleMetadata) + self.assertLen(sample.keys, 3) + for sample_key, key in zip(sample.keys, keys): + self.assertEqual(sample_key, key) + + def test_order_is_sequential_after_add(self): + keys = [1, 2, 4] + for key in keys: + self.sampler.add(key) + self.sampler.add(3) + sample = self.sampler.sample(4) + self.assertIsInstance(sample, samplers.SampleMetadata) + self.assertLen(sample.keys, 4) + keys = [1, 2, 3, 4] + for sample_key, key in zip(sample.keys, keys): + self.assertEqual(sample_key, key) + + def test_order_is_sequential_after_remove(self): + keys = [1, 2, 3, 4, 5] + for key in keys: + self.sampler.add(key) + self.sampler.remove(2) + sample = self.sampler.sample(4) + self.assertIsInstance(sample, samplers.SampleMetadata) + self.assertLen(sample.keys, 4) + keys = [1, 3, 4, 5] + for sample_key, key in zip(sample.keys, keys): + self.assertEqual(sample_key, key) + + def test_order_with_unsorted_add(self): + keys = [4, 1, 3, 5, 2] + for key in keys: + self.sampler.add(key) + sample = self.sampler.sample(5) + self.assertIsInstance(sample, samplers.SampleMetadata) + self.assertLen(sample.keys, 5) + keys = [1, 2, 3, 4, 5] + for sample_key, key in zip(sample.keys, keys): + self.assertEqual(sample_key, key) + + def test_clear_sampler(self): + for i in range(3): + self.sampler.add(i) + self.assertNotEmpty(self.sampler._key_by_index) + self.assertNotEmpty(self.sampler._index_by_key) + self.sampler.clear() + self.assertEmpty(self.sampler._key_by_index) + self.assertEmpty(self.sampler._index_by_key) + + def test_unsorted_add_with_sort_samples_false(self): + sampler = samplers.SequentialSamplingDistribution( + seed=0, sort_samples=False + ) + keys = [4, 1, 3, 5, 2] + for key in keys: + sampler.add(key) + sample = sampler.sample(5) + self.assertIsInstance(sample, samplers.SampleMetadata) + self.assertLen(sample.keys, 5) + for sample_key, key in zip(sample.keys, keys): + self.assertEqual(sample_key, key) + + +if __name__ == "__main__": + absltest.main() diff --git a/tests/dopamine/jax/replay_memory/sum_tree_test.py b/tests/dopamine/jax/replay_memory/sum_tree_test.py new file mode 100644 index 00000000..2b5f86dc --- /dev/null +++ b/tests/dopamine/jax/replay_memory/sum_tree_test.py @@ -0,0 +1,168 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for vectorized sum tree.""" + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax.replay_memory import sum_tree +import numpy as np + + +class SumTreeTest(parameterized.TestCase): + + def setUp(self): + super(SumTreeTest, self).setUp() + self._tree = sum_tree.SumTree(capacity=100) + + def test_negative_capacity_raises(self): + with self.assertRaises(AssertionError): + sum_tree.SumTree(capacity=-1) + + def test_negative_value_raises(self): + with self.assertRaises(AssertionError): + self._tree.set(0, -1) + + def test_set_small_capacity(self): + tree = sum_tree.SumTree(capacity=1) + tree.set(0, 1.5) + self.assertEqual(tree.root, 1.5) + + def test_set_and_get_value(self): + self._tree.set(0, 1.0) + self.assertEqual(self._tree.get(0), 1.0) + + # Validate that all nodes on the leftmost branch have value 1. + leaf_index = self._tree._first_leaf_offset + while leaf_index > 0: + leaf_index = leaf_index // 2 + self.assertEqual(self._tree._nodes[leaf_index], 1.0) + + def test_set_and_get_values_vectorized(self): + self._tree.set( + np.array([1, 2], dtype=np.int32), + np.array([3.0, 4.0], dtype=np.float32), + ) + self.assertEqual(self._tree.get(1), 3.0) + self.assertEqual(self._tree.get(2), 4.0) + self.assertEqual(self._tree.root, 7.0) + + def test_set_with_duplicates(self): + self._tree.set( + np.array([1, 1, 1, 2, 2], dtype=np.int32), + np.array([3.0, 3.0, 3.0, 4.0, 4.0], dtype=np.float32), + ) + self.assertEqual(self._tree.get(1), 3.0) + self.assertEqual(self._tree.get(2), 4.0) + self.assertEqual(self._tree.root, 7.0) + + def test_capacity_greater_than_requested(self): + self.assertGreaterEqual(self._tree._nodes.size, 100) + + def test_query_empty_tree(self): + with self.assertRaises(ValueError): + self._tree.query(1.0) + + def test_query_value(self): + self._tree.set(5, 1.0) + self.assertEqual(self._tree.query(0.99), 5) + + def test_query_values_vectorized(self): + # + """. + + [2.5] + [1.5] [1.0] + [0.5 1.0 0.5 0.5] + """ + tree = sum_tree.SumTree(capacity=4) + tree.set( + np.array([0, 1, 2, 3], dtype=np.int32), + np.array([0.5, 1.0, 0.5, 0.5], dtype=np.float32), + ) + self.assertEqual(tree.root, 2.5) + self.assertEqual(tree._depth, 3) + self.assertEqual(tree._nodes.size, 7) + np.testing.assert_array_equal( + tree.query(np.array([1.5, 1.0])), + np.array([2, 1], np.int32), + ) + + def test_update_sum_values(self): + # + """. + + [2.5] + [1.5] [1.0] + [0.5 1.0 0.5 0.5] + """ + tree = sum_tree.SumTree(capacity=4) + tree.set( + np.array([0, 1, 2, 3], dtype=np.int32), + np.array([0.5, 1.0, 0.5, 0.5], dtype=np.float32), + ) + tree.set(0, 0.25) + self.assertEqual(tree.root, 2.25) + self.assertEqual(tree.query(0.249), 0) + self.assertEqual(tree.query(0.5), 1) + self.assertEqual(tree.query(1.25), 2) + + def test_query_values_vectorized_large_tree(self): + # + """. + + [8] + [4] [4] + [2] [2] [2] [2] + [1, 1, 1, 1, 1, 1, 1, 1] + """ + tree = sum_tree.SumTree(capacity=8) + tree.set( + np.arange(8, dtype=np.int32), + np.ones((8,), dtype=np.float32), + ) + self.assertEqual(tree.root, 8.0) + self.assertEqual(tree._depth, 4) + self.assertEqual(tree._nodes.size, 15) + np.testing.assert_array_equal( + tree.query(np.arange(8, dtype=np.int32)), + np.arange(8, dtype=np.int32), + ) + + def test_serialization(self): + self._tree.set(5, 1.0) + state_dict = self._tree.to_state_dict() + np.testing.assert_array_equal(state_dict['nodes'], self._tree._nodes) + self._tree.from_state_dict(state_dict) + np.testing.assert_array_equal(state_dict['nodes'], self._tree._nodes) + self.assertEqual(self._tree.root, 1.0) + self.assertEqual(self._tree.get(5), 1.0) + + def test_clear(self): + self._tree.set(5, 1.0) + self._tree.clear() + self.assertEqual(self._tree.root, 0.0) + self.assertEqual(self._tree.get(5), 0.0) + + def test_max_recorded_priority(self): + k = 32 + self._tree.set(0, 0) + self.assertEqual(self._tree.max_recorded_priority, 1) + for i in range(1, k): + self._tree.set(i, i) + self.assertEqual(self._tree.max_recorded_priority, i) + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/jax/serialization_test.py b/tests/dopamine/jax/serialization_test.py new file mode 100644 index 00000000..86d3303f --- /dev/null +++ b/tests/dopamine/jax/serialization_test.py @@ -0,0 +1,74 @@ +# coding=utf-8 +# Copyright 2024 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Serialization test.""" + +from typing import Union + +from absl.testing import absltest +from absl.testing import parameterized +from dopamine.jax import serialization +import numpy as np + + +class SerializationTest(parameterized.TestCase): + + @parameterized.parameters( + (np.array([1, 2, 3], order='F'),), + (np.array([1, 2, 3], order='C'),), + (np.array([1, 2, 3]),), + (np.zeros((4, 4, 4), order='F'),), + (np.zeros((4, 4, 4), order='C'),), + (np.zeros((4, 4, 4)),), + # Structured array + ( + np.array( + [('A', 1, 1.0), ('B', 2, 2.0)], + dtype=[('string', 'U1'), ('int', 'i4'), ('float', 'f4')], + ), + ), + (np.bool_(True),), + (np.int_(1),), + (np.float64(1.0),), + ) + def testEncodeNumpy(self, array: Union[np.ndarray, np.bool_, np.number]): + encoded = serialization.encode(array) + self.assertIn('dtype', encoded) + self.assertIsInstance(encoded['dtype'], str) + self.assertIn('shape', encoded) + self.assertIsInstance(encoded['shape'], tuple) + self.assertIn('data', encoded) + self.assertIsInstance(encoded['data'], bytes) + + decoded = serialization.decode(encoded) + np.testing.assert_array_equal(array, decoded, strict=True) + + @parameterized.parameters((1,), (1234567891011121314151617181920,)) + def testEncodeLongIntegers(self, integer: int): + encoded = serialization.encode(integer) + + if integer.bit_length() > 32: + self.assertIsInstance(encoded, dict) + self.assertIn('integer', encoded) + self.assertIsInstance(encoded['integer'], str) + else: + assert isinstance(encoded, int) + assert encoded == integer + + decoded = serialization.decode(encoded) + assert decoded == integer + + +if __name__ == '__main__': + absltest.main() diff --git a/tests/dopamine/labs/atari_100k/train_test.py b/tests/dopamine/labs/atari_100k/train_test.py index 769d11c0..936fa073 100644 --- a/tests/dopamine/labs/atari_100k/train_test.py +++ b/tests/dopamine/labs/atari_100k/train_test.py @@ -35,7 +35,8 @@ def QuickAgentFlags(): 'Runner.num_iterations=1', 'Runner.max_steps_per_episode=100', 'JaxDQNAgent.min_replay_history=100', - 'OutOfGraphPrioritizedReplayBuffer.replay_capacity=10000', + 'ReplayBuffer.max_capacity=10000', + 'PrioritizedSamplingDistribution.max_capacity=10000', ] FLAGS.alsologtostderr = True diff --git a/tests/dopamine/labs/offline_rl/jax/offline_agent_test.py b/tests/dopamine/labs/offline_rl/jax/offline_agent_test.py index 99eb24d0..ccc90d16 100644 --- a/tests/dopamine/labs/offline_rl/jax/offline_agent_test.py +++ b/tests/dopamine/labs/offline_rl/jax/offline_agent_test.py @@ -103,7 +103,7 @@ def _test_train_step_updates_weights(self, agent_name): params_after = agent.online_params for i, (param1, param2) in enumerate( - zip(jax.tree_leaves(params_before), jax.tree_leaves(params_after)) + zip(jax.tree.leaves(params_before), jax.tree.leaves(params_after)) ): with self.subTest('param_set_{}'.format(i)): self.assertTrue((param1 != param2).any()) diff --git a/tests/dopamine/tests/gin_config_test.py b/tests/dopamine/tests/gin_config_test.py index c019617d..a42e9a2c 100644 --- a/tests/dopamine/tests/gin_config_test.py +++ b/tests/dopamine/tests/gin_config_test.py @@ -52,7 +52,7 @@ def testDefaultGinDqn(self): logging.info('####### DQN base_dir: %s', self._base_dir) gin_files = ['dopamine/jax/agents/dqn/configs/dqn.gin'] gin_bindings = [ - 'OutOfGraphReplayBuffer.replay_capacity = 100', # To prevent OOM. + 'ReplayBuffer.max_capacity = 100', # To prevent OOM. "create_agent.agent_name = 'jax_dqn'", ] run_experiment.load_gin_configs(gin_files, gin_bindings) @@ -74,7 +74,7 @@ def testOverrideRunnerParams(self): gin_bindings = [ 'TrainRunner.base_dir = "{}"'.format(self._base_dir), 'Runner.log_every_n = 1729', - 'OutOfGraphReplayBuffer.replay_capacity = 100', # To prevent OOM. + 'ReplayBuffer.max_capacity = 100', # To prevent OOM. "create_agent.agent_name = 'jax_dqn'", ] run_experiment.load_gin_configs(gin_files, gin_bindings) @@ -92,7 +92,7 @@ def testDefaultGinRmspropDqn(self): logging.info('####### DQN base_dir: %s', self._base_dir) gin_files = ['dopamine/jax/agents/dqn/configs/dqn.gin'] gin_bindings = [ - 'OutOfGraphReplayBuffer.replay_capacity = 100', # To prevent OOM. + 'ReplayBuffer.max_capacity = 100', # To prevent OOM. "create_agent.agent_name = 'jax_dqn'", ] run_experiment.load_gin_configs(gin_files, gin_bindings) @@ -112,7 +112,7 @@ def testOverrideGinDqn(self): logging.info('####### DQN base_dir: %s', self._base_dir) gin_files = ['dopamine/jax/agents/dqn/configs/dqn.gin'] gin_bindings = [ - 'OutOfGraphReplayBuffer.replay_capacity = 100', # To prevent OOM. + 'ReplayBuffer.max_capacity = 100', # To prevent OOM. "create_agent.agent_name = 'jax_dqn'", ] @@ -135,7 +135,7 @@ def testDefaultGinRainbow(self): 'dopamine/jax/agents/rainbow/configs/rainbow.gin' ] gin_bindings = [ - 'OutOfGraphReplayBuffer.replay_capacity = 100', # To prevent OOM. + 'ReplayBuffer.max_capacity = 100', # To prevent OOM. "create_agent.agent_name = 'jax_rainbow'", ] run_experiment.load_gin_configs(gin_files, gin_bindings) @@ -157,7 +157,7 @@ def testOverrideGinRainbow(self): 'dopamine/jax/agents/rainbow/configs/rainbow.gin', ] gin_bindings = [ - 'OutOfGraphReplayBuffer.replay_capacity = 100', # To prevent OOM. + 'ReplayBuffer.max_capacity = 100', # To prevent OOM. "create_agent.agent_name = 'jax_rainbow'", ] run_experiment.load_gin_configs(gin_files, gin_bindings) @@ -187,7 +187,7 @@ def testDefaultDQNConfig(self): self.assertEqual(agent.epsilon_train, 0.01) self.assertEqual(agent.epsilon_eval, 0.001) self.assertEqual(agent.epsilon_decay_period, 250000) - self.assertEqual(agent._replay._replay_capacity, 1000000) + self.assertEqual(agent._replay._max_capacity, 1000000) self.assertEqual(agent._replay._batch_size, 32) def testDefaultC51Config(self): @@ -211,7 +211,7 @@ def testDefaultC51Config(self): self.assertEqual(agent.epsilon_train, 0.01) self.assertEqual(agent.epsilon_eval, 0.001) self.assertEqual(agent.epsilon_decay_period, 250000) - self.assertEqual(agent._replay._replay_capacity, 1000000) + self.assertEqual(agent._replay._max_capacity, 1000000) self.assertEqual(agent._replay._batch_size, 32) def testDefaultRainbowConfig(self): @@ -235,7 +235,7 @@ def testDefaultRainbowConfig(self): self.assertEqual(agent.epsilon_train, 0.01) self.assertEqual(agent.epsilon_eval, 0.001) self.assertEqual(agent.epsilon_decay_period, 250000) - self.assertEqual(agent._replay._replay_capacity, 1000000) + self.assertEqual(agent._replay._max_capacity, 1000000) self.assertEqual(agent._replay._batch_size, 32) def testDefaultGinImplicitQuantile(self): @@ -259,7 +259,7 @@ def testDefaultGinImplicitQuantile(self): run_experiment.create_agent, atari_lib.create_atari_environment, ) - self.assertEqual(1000000, runner._agent._replay._replay_capacity) + self.assertEqual(1000000, runner._agent._replay._max_capacity) shutil.rmtree(self._base_dir) def testOverrideGinImplicitQuantile(self): @@ -276,7 +276,7 @@ def testOverrideGinImplicitQuantile(self): ] gin_bindings = [ 'Runner.num_iterations=0', - 'OutOfGraphReplayBuffer.replay_capacity = 1000', + 'ReplayBuffer.max_capacity = 1000', ] run_experiment.load_gin_configs(gin_files, gin_bindings) runner = run_experiment.Runner( @@ -284,7 +284,7 @@ def testOverrideGinImplicitQuantile(self): run_experiment.create_agent, atari_lib.create_atari_environment, ) - self.assertEqual(1000, runner._agent._replay._replay_capacity) + self.assertEqual(1000, runner._agent._replay._max_capacity) shutil.rmtree(self._base_dir) diff --git a/tests/dopamine/tests/train_runner_integration_test.py b/tests/dopamine/tests/train_runner_integration_test.py index 53c5e241..d0ae103d 100644 --- a/tests/dopamine/tests/train_runner_integration_test.py +++ b/tests/dopamine/tests/train_runner_integration_test.py @@ -40,7 +40,7 @@ def setUp(self): self._checkpoint_dir = os.path.join(FLAGS.base_dir, 'checkpoints') self._logging_dir = os.path.join(FLAGS.base_dir, 'logs') - def quickDqnFlags(self): + def quick_dqn_flags(self): """Assign flags for a quick run of DQN agent.""" FLAGS.gin_files = ['dopamine/jax/agents/dqn/configs/dqn.gin'] FLAGS.gin_bindings = [ @@ -54,7 +54,7 @@ def quickDqnFlags(self): ] FLAGS.alsologtostderr = True - def verifyFilesCreated(self, base_dir): + def verify_files_created(self): """Verify that files have been created.""" # Check checkpoint files self.assertTrue( @@ -72,9 +72,9 @@ def testIntegrationDqn(self): """Test the DQN agent.""" logging.info('####### Training the DQN agent #####') logging.info('####### DQN base_dir: %s', FLAGS.base_dir) - self.quickDqnFlags() + self.quick_dqn_flags() train.main([]) - self.verifyFilesCreated(FLAGS.base_dir) + self.verify_files_created() shutil.rmtree(FLAGS.base_dir) diff --git a/tests/dopamine/agents/dqn/dqn_agent_test.py b/tests/tf/agents/dqn/dqn_agent_test.py similarity index 99% rename from tests/dopamine/agents/dqn/dqn_agent_test.py rename to tests/tf/agents/dqn/dqn_agent_test.py index 4480d72c..b271f583 100644 --- a/tests/dopamine/agents/dqn/dqn_agent_test.py +++ b/tests/tf/agents/dqn/dqn_agent_test.py @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Tests for dopamine.agents.dqn_agent.""" +"""Tests for dopamine.tf.agents.dqn_agent.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -23,8 +23,8 @@ from absl import flags -from dopamine.agents.dqn import dqn_agent from dopamine.discrete_domains import atari_lib +from dopamine.tf.agents.dqn import dqn_agent from dopamine.utils import test_utils import gin import mock diff --git a/tests/dopamine/agents/implicit_quantile/implicit_quantile_agent_test.py b/tests/tf/agents/implicit_quantile/implicit_quantile_agent_test.py similarity index 98% rename from tests/dopamine/agents/implicit_quantile/implicit_quantile_agent_test.py rename to tests/tf/agents/implicit_quantile/implicit_quantile_agent_test.py index fd2cab32..413603f2 100644 --- a/tests/dopamine/agents/implicit_quantile/implicit_quantile_agent_test.py +++ b/tests/tf/agents/implicit_quantile/implicit_quantile_agent_test.py @@ -20,9 +20,9 @@ -from dopamine.agents.dqn import dqn_agent -from dopamine.agents.implicit_quantile import implicit_quantile_agent from dopamine.discrete_domains import atari_lib +from dopamine.tf.agents.dqn import dqn_agent +from dopamine.tf.agents.implicit_quantile import implicit_quantile_agent import numpy as np import tensorflow as tf diff --git a/tests/dopamine/agents/rainbow/rainbow_agent_test.py b/tests/tf/agents/rainbow/rainbow_agent_test.py similarity index 99% rename from tests/dopamine/agents/rainbow/rainbow_agent_test.py rename to tests/tf/agents/rainbow/rainbow_agent_test.py index 8f5e8cc5..22a32e7a 100644 --- a/tests/dopamine/agents/rainbow/rainbow_agent_test.py +++ b/tests/tf/agents/rainbow/rainbow_agent_test.py @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Tests for dopamine.agents.rainbow.rainbow_agent.""" +"""Tests for dopamine.tf.agents.rainbow.rainbow_agent.""" from __future__ import absolute_import from __future__ import division @@ -20,9 +20,9 @@ -from dopamine.agents.dqn import dqn_agent -from dopamine.agents.rainbow import rainbow_agent from dopamine.discrete_domains import atari_lib +from dopamine.tf.agents.dqn import dqn_agent +from dopamine.tf.agents.rainbow import rainbow_agent from dopamine.utils import test_utils import numpy as np import tensorflow as tf diff --git a/tests/dopamine/replay_memory/circular_replay_buffer_test.py b/tests/tf/replay_memory/circular_replay_buffer_test.py similarity index 99% rename from tests/dopamine/replay_memory/circular_replay_buffer_test.py rename to tests/tf/replay_memory/circular_replay_buffer_test.py index 9dcac924..e030cb68 100644 --- a/tests/dopamine/replay_memory/circular_replay_buffer_test.py +++ b/tests/tf/replay_memory/circular_replay_buffer_test.py @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Tests for circular_replay_buffer.py.""" +"""Tests for (deprecated) Circular replay buffer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -24,7 +24,7 @@ from absl import flags -from dopamine.replay_memory import circular_replay_buffer +from dopamine.tf.replay_memory import circular_replay_buffer import numpy as np import tensorflow as tf diff --git a/tests/dopamine/replay_memory/prioritized_replay_buffer_test.py b/tests/tf/replay_memory/prioritized_replay_buffer_test.py similarity index 96% rename from tests/dopamine/replay_memory/prioritized_replay_buffer_test.py rename to tests/tf/replay_memory/prioritized_replay_buffer_test.py index 515b43ac..b672534b 100644 --- a/tests/dopamine/replay_memory/prioritized_replay_buffer_test.py +++ b/tests/tf/replay_memory/prioritized_replay_buffer_test.py @@ -12,15 +12,15 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Tests for prioritzed replay memory.""" +"""Tests for (deprecated) prioritzed replay memory.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function -from dopamine.replay_memory import circular_replay_buffer -from dopamine.replay_memory import prioritized_replay_buffer +from dopamine.tf.replay_memory import circular_replay_buffer +from dopamine.tf.replay_memory import prioritized_replay_buffer import numpy as np import tensorflow as tf @@ -74,7 +74,7 @@ def testAddWithAndWithoutPriority(self): # Check that the prioritized replay buffer expects an additional argument # for priority. - with self.assertRaisesRegexp(ValueError, 'Add expects'): + with self.assertRaisesRegex(ValueError, 'Add expects'): memory.add(zeros, 0, 0, 0) def testAddWithAdditionalArgsAndPriority(self): @@ -92,7 +92,7 @@ def testAddWithAdditionalArgsAndPriority(self): # Check that the prioritized replay buffer expects an additional argument # for test_item. - with self.assertRaisesRegexp(ValueError, 'Add expects'): + with self.assertRaisesRegex(ValueError, 'Add expects'): memory.add(zeros, 0, 0, 0, priority=1.0) def testDummyScreensAddedToNewMemory(self): diff --git a/tests/dopamine/replay_memory/sum_tree_test.py b/tests/tf/replay_memory/sum_tree_test.py similarity index 96% rename from tests/dopamine/replay_memory/sum_tree_test.py rename to tests/tf/replay_memory/sum_tree_test.py index 24d81d70..128233dc 100644 --- a/tests/dopamine/replay_memory/sum_tree_test.py +++ b/tests/tf/replay_memory/sum_tree_test.py @@ -12,7 +12,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Tests for dopamine.agents.rainbow.sum_tree.""" +"""Tests for the (deprecated) sum tree.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function @@ -21,7 +21,7 @@ -from dopamine.replay_memory import sum_tree +from dopamine.tf.replay_memory import sum_tree import tensorflow as tf @@ -45,9 +45,9 @@ def testSetNegativeValue(self): def testSmallCapacityConstructor(self): tree = sum_tree.SumTree(capacity=1) - self.assertEqual(len(tree.nodes), 1) + self.assertLen(tree.nodes, 1) tree = sum_tree.SumTree(capacity=2) - self.assertEqual(len(tree.nodes), 2) + self.assertLen(tree.nodes, 2) def testSetValueSmallCapacity(self): tree = sum_tree.SumTree(capacity=1) @@ -146,7 +146,7 @@ def testStratifiedSampling(self): for i in range(k): self._tree.set(node_index=i, value=1) samples = self._tree.stratified_sample(k) - self.assertEqual(len(samples), k) + self.assertLen(samples, k) for i in range(k): self.assertEqual(samples[i], i) diff --git a/dopamine/agents/dqn/__init__.py b/tf/agents/__init__.py similarity index 100% rename from dopamine/agents/dqn/__init__.py rename to tf/agents/__init__.py diff --git a/dopamine/agents/implicit_quantile/__init__.py b/tf/agents/dqn/__init__.py similarity index 100% rename from dopamine/agents/implicit_quantile/__init__.py rename to tf/agents/dqn/__init__.py diff --git a/dopamine/agents/dqn/configs/dqn.gin b/tf/agents/dqn/configs/dqn.gin similarity index 93% rename from dopamine/agents/dqn/configs/dqn.gin rename to tf/agents/dqn/configs/dqn.gin index 13b5727d..fa91e3a9 100644 --- a/dopamine/agents/dqn/configs/dqn.gin +++ b/tf/agents/dqn/configs/dqn.gin @@ -3,8 +3,8 @@ # comparison. import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.gamma = 0.99 diff --git a/dopamine/agents/dqn/configs/dqn_acrobot.gin b/tf/agents/dqn/configs/dqn_acrobot.gin similarity index 92% rename from dopamine/agents/dqn/configs/dqn_acrobot.gin rename to tf/agents/dqn/configs/dqn_acrobot.gin index 2a53f7c2..52926d62 100644 --- a/dopamine/agents/dqn/configs/dqn_acrobot.gin +++ b/tf/agents/dqn/configs/dqn_acrobot.gin @@ -2,8 +2,8 @@ # chosen achieve reasonable performance. import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE diff --git a/dopamine/agents/dqn/configs/dqn_cartpole.gin b/tf/agents/dqn/configs/dqn_cartpole.gin similarity index 93% rename from dopamine/agents/dqn/configs/dqn_cartpole.gin rename to tf/agents/dqn/configs/dqn_cartpole.gin index b80d4cce..875aa9ba 100644 --- a/dopamine/agents/dqn/configs/dqn_cartpole.gin +++ b/tf/agents/dqn/configs/dqn_cartpole.gin @@ -2,8 +2,8 @@ # chosen achieve reasonable performance. import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE diff --git a/dopamine/agents/dqn/configs/dqn_icml.gin b/tf/agents/dqn/configs/dqn_icml.gin similarity index 93% rename from dopamine/agents/dqn/configs/dqn_icml.gin rename to tf/agents/dqn/configs/dqn_icml.gin index eea97962..e9a50a8b 100644 --- a/dopamine/agents/dqn/configs/dqn_icml.gin +++ b/tf/agents/dqn/configs/dqn_icml.gin @@ -1,8 +1,8 @@ # Hyperparameters used for reporting DQN results in Bellemare et al. (2017). import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.gamma = 0.99 diff --git a/dopamine/agents/dqn/configs/dqn_lunarlander.gin b/tf/agents/dqn/configs/dqn_lunarlander.gin similarity index 92% rename from dopamine/agents/dqn/configs/dqn_lunarlander.gin rename to tf/agents/dqn/configs/dqn_lunarlander.gin index c4a5a130..d42927ba 100644 --- a/dopamine/agents/dqn/configs/dqn_lunarlander.gin +++ b/tf/agents/dqn/configs/dqn_lunarlander.gin @@ -2,8 +2,8 @@ # chosen achieve reasonable performance. import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.observation_shape = %gym_lib.LUNAR_OBSERVATION_SHAPE diff --git a/dopamine/agents/dqn/configs/dqn_mountaincar.gin b/tf/agents/dqn/configs/dqn_mountaincar.gin similarity index 93% rename from dopamine/agents/dqn/configs/dqn_mountaincar.gin rename to tf/agents/dqn/configs/dqn_mountaincar.gin index dee3d6b5..b90423d5 100644 --- a/dopamine/agents/dqn/configs/dqn_mountaincar.gin +++ b/tf/agents/dqn/configs/dqn_mountaincar.gin @@ -2,8 +2,8 @@ # chosen achieve reasonable performance. import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.observation_shape = %gym_lib.MOUNTAINCAR_OBSERVATION_SHAPE diff --git a/dopamine/agents/dqn/configs/dqn_nature.gin b/tf/agents/dqn/configs/dqn_nature.gin similarity index 93% rename from dopamine/agents/dqn/configs/dqn_nature.gin rename to tf/agents/dqn/configs/dqn_nature.gin index 4cf4664f..fa3cee1b 100644 --- a/dopamine/agents/dqn/configs/dqn_nature.gin +++ b/tf/agents/dqn/configs/dqn_nature.gin @@ -1,8 +1,8 @@ # Hyperparameters used in Mnih et al. (2015). import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.gamma = 0.99 diff --git a/dopamine/agents/dqn/configs/dqn_profiling.gin b/tf/agents/dqn/configs/dqn_profiling.gin similarity index 93% rename from dopamine/agents/dqn/configs/dqn_profiling.gin rename to tf/agents/dqn/configs/dqn_profiling.gin index a253fb77..da6611cf 100644 --- a/dopamine/agents/dqn/configs/dqn_profiling.gin +++ b/tf/agents/dqn/configs/dqn_profiling.gin @@ -3,8 +3,8 @@ # comparison. import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.agents.dqn.dqn_agent -import dopamine.replay_memory.circular_replay_buffer +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.replay_memory.circular_replay_buffer import gin.tf.external_configurables DQNAgent.gamma = 0.99 diff --git a/dopamine/agents/dqn/dqn_agent.py b/tf/agents/dqn/dqn_agent.py similarity index 99% rename from dopamine/agents/dqn/dqn_agent.py rename to tf/agents/dqn/dqn_agent.py index af239475..a745fdc1 100644 --- a/dopamine/agents/dqn/dqn_agent.py +++ b/tf/agents/dqn/dqn_agent.py @@ -24,7 +24,7 @@ from absl import logging from dopamine.discrete_domains import atari_lib -from dopamine.replay_memory import circular_replay_buffer +from dopamine.tf.replay_memory import circular_replay_buffer import gin import numpy as np import tensorflow as tf diff --git a/dopamine/agents/rainbow/__init__.py b/tf/agents/implicit_quantile/__init__.py similarity index 100% rename from dopamine/agents/rainbow/__init__.py rename to tf/agents/implicit_quantile/__init__.py diff --git a/dopamine/agents/implicit_quantile/configs/implicit_quantile.gin b/tf/agents/implicit_quantile/configs/implicit_quantile.gin similarity index 89% rename from dopamine/agents/implicit_quantile/configs/implicit_quantile.gin rename to tf/agents/implicit_quantile/configs/implicit_quantile.gin index 76b37f75..290e3d66 100644 --- a/dopamine/agents/implicit_quantile/configs/implicit_quantile.gin +++ b/tf/agents/implicit_quantile/configs/implicit_quantile.gin @@ -1,11 +1,11 @@ # Hyperparameters follow Dabney et al. (2018), but we modify as necessary to # match those used in Rainbow (Hessel et al., 2018), to ensure apples-to-apples # comparison. -import dopamine.agents.implicit_quantile.implicit_quantile_agent -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.implicit_quantile.implicit_quantile_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables ImplicitQuantileAgent.kappa = 1.0 diff --git a/dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin b/tf/agents/implicit_quantile/configs/implicit_quantile_icml.gin similarity index 88% rename from dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin rename to tf/agents/implicit_quantile/configs/implicit_quantile_icml.gin index 61761f6c..47dcc722 100644 --- a/dopamine/agents/implicit_quantile/configs/implicit_quantile_icml.gin +++ b/tf/agents/implicit_quantile/configs/implicit_quantile_icml.gin @@ -1,9 +1,9 @@ # Hyperparameters follow Dabney et al. (2018). -import dopamine.agents.implicit_quantile.implicit_quantile_agent -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.implicit_quantile.implicit_quantile_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables ImplicitQuantileAgent.kappa = 1.0 diff --git a/dopamine/agents/implicit_quantile/configs/implicit_quantile_profiling.gin b/tf/agents/implicit_quantile/configs/implicit_quantile_profiling.gin similarity index 90% rename from dopamine/agents/implicit_quantile/configs/implicit_quantile_profiling.gin rename to tf/agents/implicit_quantile/configs/implicit_quantile_profiling.gin index cbcb6353..6d309ef4 100644 --- a/dopamine/agents/implicit_quantile/configs/implicit_quantile_profiling.gin +++ b/tf/agents/implicit_quantile/configs/implicit_quantile_profiling.gin @@ -1,11 +1,11 @@ # Hyperparameters follow Dabney et al. (2018), but we modify as necessary to # match those used in Rainbow (Hessel et al., 2018), to ensure apples-to-apples # comparison. -import dopamine.agents.implicit_quantile.implicit_quantile_agent -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.implicit_quantile.implicit_quantile_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables ImplicitQuantileAgent.kappa = 1.0 diff --git a/dopamine/agents/implicit_quantile/implicit_quantile_agent.py b/tf/agents/implicit_quantile/implicit_quantile_agent.py similarity index 99% rename from dopamine/agents/implicit_quantile/implicit_quantile_agent.py rename to tf/agents/implicit_quantile/implicit_quantile_agent.py index 4aa318ba..6cb1b4b0 100644 --- a/dopamine/agents/implicit_quantile/implicit_quantile_agent.py +++ b/tf/agents/implicit_quantile/implicit_quantile_agent.py @@ -24,8 +24,8 @@ -from dopamine.agents.rainbow import rainbow_agent from dopamine.discrete_domains import atari_lib +from dopamine.tf.agents.rainbow import rainbow_agent import gin import tensorflow as tf diff --git a/dopamine/replay_memory/__init__.py b/tf/agents/rainbow/__init__.py similarity index 100% rename from dopamine/replay_memory/__init__.py rename to tf/agents/rainbow/__init__.py diff --git a/dopamine/agents/rainbow/configs/c51.gin b/tf/agents/rainbow/configs/c51.gin similarity index 93% rename from dopamine/agents/rainbow/configs/c51.gin rename to tf/agents/rainbow/configs/c51.gin index 4374e826..e85855fd 100644 --- a/dopamine/agents/rainbow/configs/c51.gin +++ b/tf/agents/rainbow/configs/c51.gin @@ -1,10 +1,10 @@ # Hyperparameters follow the settings from Bellemare et al. (2017), but we # modify as necessary to match those used in Rainbow (Hessel et al., 2018), to # ensure apples-to-apples comparison. -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.num_atoms = 51 diff --git a/dopamine/agents/rainbow/configs/c51_acrobot.gin b/tf/agents/rainbow/configs/c51_acrobot.gin similarity index 90% rename from dopamine/agents/rainbow/configs/c51_acrobot.gin rename to tf/agents/rainbow/configs/c51_acrobot.gin index 970d16da..c54bb535 100644 --- a/dopamine/agents/rainbow/configs/c51_acrobot.gin +++ b/tf/agents/rainbow/configs/c51_acrobot.gin @@ -1,10 +1,10 @@ # Hyperparameters for a simple C51-style Acrobot agent. The hyperparameters # chosen achieve reasonable performance. -import dopamine.agents.dqn.dqn_agent -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE diff --git a/dopamine/agents/rainbow/configs/c51_cartpole.gin b/tf/agents/rainbow/configs/c51_cartpole.gin similarity index 91% rename from dopamine/agents/rainbow/configs/c51_cartpole.gin rename to tf/agents/rainbow/configs/c51_cartpole.gin index 782c7981..69746740 100644 --- a/dopamine/agents/rainbow/configs/c51_cartpole.gin +++ b/tf/agents/rainbow/configs/c51_cartpole.gin @@ -1,10 +1,10 @@ # Hyperparameters for a simple C51-style Cartpole agent. The hyperparameters # chosen achieve reasonable performance. -import dopamine.agents.dqn.dqn_agent -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE diff --git a/dopamine/agents/rainbow/configs/c51_icml.gin b/tf/agents/rainbow/configs/c51_icml.gin similarity index 92% rename from dopamine/agents/rainbow/configs/c51_icml.gin rename to tf/agents/rainbow/configs/c51_icml.gin index e148591c..36798112 100644 --- a/dopamine/agents/rainbow/configs/c51_icml.gin +++ b/tf/agents/rainbow/configs/c51_icml.gin @@ -1,8 +1,8 @@ # Hyperparameters used in Bellemare et al. (2017). -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.num_atoms = 51 diff --git a/dopamine/agents/rainbow/configs/c51_profiling.gin b/tf/agents/rainbow/configs/c51_profiling.gin similarity index 93% rename from dopamine/agents/rainbow/configs/c51_profiling.gin rename to tf/agents/rainbow/configs/c51_profiling.gin index 02463076..705669f4 100644 --- a/dopamine/agents/rainbow/configs/c51_profiling.gin +++ b/tf/agents/rainbow/configs/c51_profiling.gin @@ -1,10 +1,10 @@ # Hyperparameters follow the settings from Bellemare et al. (2017), but we # modify as necessary to match those used in Rainbow (Hessel et al., 2018), to # ensure apples-to-apples comparison. -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.num_atoms = 51 diff --git a/dopamine/agents/rainbow/configs/rainbow.gin b/tf/agents/rainbow/configs/rainbow.gin similarity index 93% rename from dopamine/agents/rainbow/configs/rainbow.gin rename to tf/agents/rainbow/configs/rainbow.gin index 73fe1348..adfe18fa 100644 --- a/dopamine/agents/rainbow/configs/rainbow.gin +++ b/tf/agents/rainbow/configs/rainbow.gin @@ -1,9 +1,9 @@ # Hyperparameters follow Hessel et al. (2018), except for sticky_actions, # which was False (not using sticky actions) in the original paper. -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.num_atoms = 51 diff --git a/dopamine/agents/rainbow/configs/rainbow_aaai.gin b/tf/agents/rainbow/configs/rainbow_aaai.gin similarity index 96% rename from dopamine/agents/rainbow/configs/rainbow_aaai.gin rename to tf/agents/rainbow/configs/rainbow_aaai.gin index 60ea72db..083a0fa2 100644 --- a/dopamine/agents/rainbow/configs/rainbow_aaai.gin +++ b/tf/agents/rainbow/configs/rainbow_aaai.gin @@ -1,5 +1,5 @@ # Hyperparameters follow Hessel et al. (2018). -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment import dopamine.replay_memory.prioritized_replay_buffer diff --git a/dopamine/agents/rainbow/configs/rainbow_acrobot.gin b/tf/agents/rainbow/configs/rainbow_acrobot.gin similarity index 93% rename from dopamine/agents/rainbow/configs/rainbow_acrobot.gin rename to tf/agents/rainbow/configs/rainbow_acrobot.gin index 90a7d20c..20802a7f 100644 --- a/dopamine/agents/rainbow/configs/rainbow_acrobot.gin +++ b/tf/agents/rainbow/configs/rainbow_acrobot.gin @@ -1,9 +1,9 @@ # Hyperparameters for a simple Rainbow-style Acrobot agent. The hyperparameters # chosen achieve reasonable performance. -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.observation_shape = %gym_lib.ACROBOT_OBSERVATION_SHAPE diff --git a/dopamine/agents/rainbow/configs/rainbow_cartpole.gin b/tf/agents/rainbow/configs/rainbow_cartpole.gin similarity index 90% rename from dopamine/agents/rainbow/configs/rainbow_cartpole.gin rename to tf/agents/rainbow/configs/rainbow_cartpole.gin index 6d3422e5..af6f934a 100644 --- a/dopamine/agents/rainbow/configs/rainbow_cartpole.gin +++ b/tf/agents/rainbow/configs/rainbow_cartpole.gin @@ -1,10 +1,10 @@ # Hyperparameters for a simple Rainbow-style Cartpole agent. The # hyperparameters chosen achieve reasonable performance. -import dopamine.agents.dqn.dqn_agent -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.dqn.dqn_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.gym_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.observation_shape = %gym_lib.CARTPOLE_OBSERVATION_SHAPE diff --git a/dopamine/agents/rainbow/configs/rainbow_profiling.gin b/tf/agents/rainbow/configs/rainbow_profiling.gin similarity index 93% rename from dopamine/agents/rainbow/configs/rainbow_profiling.gin rename to tf/agents/rainbow/configs/rainbow_profiling.gin index 91c50f09..b97f04c2 100644 --- a/dopamine/agents/rainbow/configs/rainbow_profiling.gin +++ b/tf/agents/rainbow/configs/rainbow_profiling.gin @@ -1,9 +1,9 @@ # Hyperparameters follow Hessel et al. (2018), except for sticky_actions, # which was False (not using sticky actions) in the original paper. -import dopamine.agents.rainbow.rainbow_agent +import dopamine.tf.agents.rainbow.rainbow_agent import dopamine.discrete_domains.atari_lib import dopamine.discrete_domains.run_experiment -import dopamine.replay_memory.prioritized_replay_buffer +import dopamine.tf.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.num_atoms = 51 diff --git a/dopamine/agents/rainbow/rainbow_agent.py b/tf/agents/rainbow/rainbow_agent.py similarity index 99% rename from dopamine/agents/rainbow/rainbow_agent.py rename to tf/agents/rainbow/rainbow_agent.py index 4f35f608..a74792fd 100644 --- a/dopamine/agents/rainbow/rainbow_agent.py +++ b/tf/agents/rainbow/rainbow_agent.py @@ -39,9 +39,9 @@ -from dopamine.agents.dqn import dqn_agent from dopamine.discrete_domains import atari_lib -from dopamine.replay_memory import prioritized_replay_buffer +from dopamine.tf.agents.dqn import dqn_agent +from dopamine.tf.replay_memory import prioritized_replay_buffer import gin import tensorflow as tf diff --git a/tf/replay_memory/__init__.py b/tf/replay_memory/__init__.py new file mode 100644 index 00000000..f9bcb7c5 --- /dev/null +++ b/tf/replay_memory/__init__.py @@ -0,0 +1,14 @@ +# coding=utf-8 +# Copyright 2018 The Dopamine Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/dopamine/replay_memory/circular_replay_buffer.py b/tf/replay_memory/circular_replay_buffer.py similarity index 100% rename from dopamine/replay_memory/circular_replay_buffer.py rename to tf/replay_memory/circular_replay_buffer.py diff --git a/dopamine/replay_memory/prioritized_replay_buffer.py b/tf/replay_memory/prioritized_replay_buffer.py similarity index 98% rename from dopamine/replay_memory/prioritized_replay_buffer.py rename to tf/replay_memory/prioritized_replay_buffer.py index 7bbf0e2d..db4f1f0f 100644 --- a/dopamine/replay_memory/prioritized_replay_buffer.py +++ b/tf/replay_memory/prioritized_replay_buffer.py @@ -24,9 +24,9 @@ -from dopamine.replay_memory import circular_replay_buffer -from dopamine.replay_memory import sum_tree -from dopamine.replay_memory.circular_replay_buffer import ReplayElement +from dopamine.tf.replay_memory import circular_replay_buffer +from dopamine.tf.replay_memory import sum_tree +from dopamine.tf.replay_memory.circular_replay_buffer import ReplayElement import gin import numpy as np import tensorflow as tf diff --git a/dopamine/replay_memory/sum_tree.py b/tf/replay_memory/sum_tree.py similarity index 98% rename from dopamine/replay_memory/sum_tree.py rename to tf/replay_memory/sum_tree.py index b0bb4885..ca2dd60d 100644 --- a/dopamine/replay_memory/sum_tree.py +++ b/tf/replay_memory/sum_tree.py @@ -115,7 +115,7 @@ def sample(self, query_value=None): the supplied query_value is larger than the total sum. """ if self._total_priority() == 0.0: - raise Exception('Cannot sample from an empty sum tree.') + raise ValueError('Cannot sample from an empty sum tree.') if query_value and (query_value < 0.0 or query_value > 1.0): raise ValueError('query_value must be in [0, 1].') @@ -159,7 +159,7 @@ def stratified_sample(self, batch_size): Exception: If the sum tree is empty (i.e. its node values sum to 0). """ if self._total_priority() == 0.0: - raise Exception('Cannot sample from an empty sum tree.') + raise ValueError('Cannot sample from an empty sum tree.') bounds = np.linspace(0.0, 1.0, batch_size + 1) assert len(bounds) == batch_size + 1