RL-pytorch

Re-implementations of Deep Reinforcement Learning (DRL) algorithms, written in PyTorch.

Installation

git clone https://github.com/BepfCp/RL-pytorch.git
cd rlpyt
pip install -e .

Implemented Algorithms

• [x] Deep Q Networks (DQN) [paper] [official code]
• [x] Deep Double Q Networks (DDQN) [paper]
• [x] Dueling Network Architectures for Deep Reinforcement Learning (DuelDQN) [paper]
• [x] Continuous control with deep reinforcement learning (DDPG) [paper]
• [x] Addressing Function Approximation Error in Actor-Critic Methods (TD3) [paper] [official code]
• [x] Soft Actor-Critic Algorithms and Applications (SAC) [paper] [official code]
• [x] Trust Region Policy Optimization (TRPO) [paper] [official code]
• [x] Proximal Policy Optimization (PPO) [paper] [official code]

Run Experiments

python scripts/train_agent.py agent=ppo env.id=Hopper-v4

By default, the results are stored at the runs dir.

Acknowledgement

With the progress of this project, I found many open-source materials on the Internet to be excellent references. I am deeply grateful for the efforts of their authors. Below is a detailed list. Additionally, I would like to extend my thanks to my friends from LAMDA-RL for our helpful discussions.

Codebase

• tianshou
• stable-baselines3
• stable-baselines-contrib
• stable-baselines
• spinningup
• RL-Adventure2
• unstable_baselines
• d4rl_evaluations
• TD3
• pytorch-trpo

Blog

• The 37 Implementation Details of Proximal Policy Optimization

Tutorial

• OpenAI SpinningUp