This repository will implement the classic deep reinforcement learning algorithms by using PyTorch. The aim of this repository is to provide clear code for people to learn the deep reinforcemen learning algorithms. In the future, more algorithms will be added and the existing codes will also be maintained.
🚩 2018-10-17 - In this update, most of algorithms have been imporved and add more experiments with plots (except for DPPG). The PPO now supports atari-games and mujoco-env. The TRPO is much stable and can have better results!
🚩 2019-07-15 - In this update, the installation for the openai baseline is no longer needed. I have intergated useful functions in the rl__utils module. DDPG is also re-implemented and support more results. README file has been modified. The code structure also has tiny adjustment.
🚩 2019-07-26 - In this update, the revised repository will be public. In order to have a light size of the repository. I rebuild the repository and the previous version is deleted. But I will make a backup in the google driver.
🚩 2019-11-13 - Change the code structure of the repo, all algorithms have been moved to
rl_algorithms/ folder. Add soft actor critic method, the expriments plots will be added soon.
pip install -e .
sudo apt-get install swig or brew install swig pip install gym[atari] pip install gym[box2d] pip install box2d box2d-kengz
cd rl_algorithms/<target_algo_folder>/ python train.py --<arguments you need>
cd rl_algorithms/<target_algo_folder>/ python demo.py --<arguments you need>
arguments.py: contain the parameters used in the training.
<rl-name>_agent.py: contain the most important part of the reinforcement learning algorithms.
models.py: the network structure for the policy and value function.
utils.py: some useful function, such as select actions.
train.py: the script to train the agent.
demo.py: visualize the trained agent.
env_wrapper/: contain the pre-processing function for the atari games and wrapper to create environments.
experience_replay/: contain the experience replay for the off-policy rl algorithms.
logger/: contain functions to take down log infos during training.
mpi_utils/: contain the tools for the mpi training.
running_filter/: contain the running mean filter functions to normalize the observation in the mujoco environments.
seeds/: contain function to setup the random seeds for the training for reproducibility.
|Atari Env (BreakoutNoFrameskip-v4)||Box2d Env (BipedalWalker-v2)||Mujoco Env (Hopper-v2)|
 A Brief Survey of Deep Reinforcement Learning
 The Beta Policy for Continuous Control Reinforcement Learning
 Playing Atari with Deep Reinforcement Learning
 Deep Reinforcement Learning with Double Q-learning
 Dueling Network Architectures for Deep Reinforcement Learning
 Continuous control with deep reinforcement learning
 Continuous Deep Q-Learning with Model-based Acceleration
 Asynchronous Methods for Deep Reinforcement Learning
 Trust Region Policy Optimization
 Proximal Policy Optimization Algorithms
 Soft Actor-Critic Algorithms and Applications
 Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation