Reinforcement learning based agents to playing Atari games.
-
Deep Q-network(DQN)
- Double DQN
- Prioritized Replay
- Dueiling network
-
Asynchronous Advantage Actor-Critic(A3C)
- Multiprocess support
- Multiprocess Cuda support
- LSTM based model
- Generalized Advantage Estimate (GAE)
- Frame stacking based model
- BreakoutDeterministic-v4
- PongDeterministic-v4
- KungFuMasterDeterministic-v4
- BoxingDeterministic-v4
- SapecInvadersDeterministic-v4
- DQN
- TensorBoard support
- Double DQN
- Prioritized replay
- Dueling network
- Train model for Pong
- Achive 300+ score on breakout
- A3C Agent for KungFuMasterDeterministic-v4
- A3C Agent for BoxingDeterministic-v4
- Parallel processing for A3C
- LSTM layer for A3C to replace frame stacking
The Apache-2.0 License. Please see the license file for more information.
- https://github.com/yandexdataschool/Practical_RL
- https://github.com/openai/baselines/blob/master/baselines/deepq/replay_buffer.py
- https://towardsdatascience.com/tutorial-double-deep-q-learning-with-dueling-network-architectures-4c1b3fb7f756
- https://pytorch.org/docs/stable/notes/multiprocessing.html
- https://github.com/ikostrikov/pytorch-a3c
- https://arxiv.org/pdf/1506.02438.pdf
- https://arxiv.org/abs/1602.01783