Population Based Training (in PyTorch with sqlite3). Status: Unsupported

A simple PyTorch implementation of Population Based Training of Neural Networks.

Reproducing results from DeepMind's paper on Population Based Training of Neural Networks.

Vectorization techniques for fast population-based training.

Population-Based Training (PBT) for Reinforcement Learning using Message Passing Interface (MPI)

A Population Based Reinforcement Learning Library based on PyTorch

Population Based Training, Figure 2

EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.

Training in bursts for defending against adversarial policies

(AAAI24 oral) Implementation of RPPO(Risk-sensitive PPO) and RPBT(Population-based self-play with RPPO)

Population-Based Training (PBT) implementation on ddpg

Applying Population Based Training on Generative Adversarial Networks.

My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].

Generating Evolutionary Opponents as a Reinforcement Guided Exploration Solution

Jupyter notebooks to play around with population based training, as described in https://arxiv.org/abs/1711.09846

🥕 Mastering flappy bird with machine learning (neural networks, neuro-evolution)

Curriculum training a Differentiable Neural Computer using Population Based Training

Implementation of the Google DeepMind paper introducing population-based training, except applied to simulated annealing instead of neural networks.

Optimize for topology, hyperparameters, and weights of Neural Nets in single joint training process using Augmented Population Based Training

Article 1 code with bonus Population Based Training support