Modularized implementation of popular deep RL algorithms by PyTorch. Easy switch between classical control tasks (e.g., CartPole) and Atari games with raw pixel inputs.

Implemented algorithms:

• (Double/Dueling) Deep Q-Learning (DQN)
• Categorical DQN (C51, Distributional DQN with KL Distance)
• Quantile Regression DQN
• (Continuous/Discrete) Synchronous Advantage Actor Critic (A2C)
• Synchronous N-Step Q-Learning
• Deep Deterministic Policy Gradient (DDPG, pixel & low-dim-state)
• (Continuous/Discrete) Synchronous Proximal Policy Optimization (PPO, pixel & low-dim-state)
• The Option-Critic Architecture (OC)
• Action Conditional Video Prediction

Asynchronous algorithms (e.g., A3C) are removed in the current version but can be found in v0.1.

• MacOS 10.12 or Ubuntu 16.04
• PyTorch v0.4.0
• Python 3.6, 3.5
• Core dependencies: pip install -e .
• Optional: Roboschool, PyBullet

• There is a super fast DQN implementation with an async actor for data generation and an async replay buffer to transfer data to GPU. Enable this implementation by setting config.async_actor = True and using AsyncReplay. However, with atari games this fast implementation may not work in macOS. Use Ubuntu or Docker instead.
• Python 2 is not officially supported after v0.3. However, I do expect most of the code will still work well in Python 2.
• Although there is a setup.py, which means you can install the repo as a library, this repo is never designed to be a high-level library like Keras. Use it as your codebase instead.

examples.py contains examples for all the implemented algorithms

Dockerfile contains an example environment (w/ pybullet, w/ roboschool, w/o GPU)

Please use this bibtex if you want to cite this repo

@misc{deeprl,
  author = {Shangtong, Zhang},
  title = {Modularized Implementation of Deep RL Algorithms in PyTorch},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/ShangtongZhang/DeepRL}},
}

• This is my synchronous option-critic implementation, not the original one.
• The curves are not directly comparable, as many hyper-parameters are different.

• The DDPG curve is the evaluation performance, rather than online.

• Left: One-step prediction Right: Ground truth
• Prediction images are sampled after 110K iterations, and I only implemented one-step training for action-conditional-video-prediction.

• Human Level Control through Deep Reinforcement Learning
• Asynchronous Methods for Deep Reinforcement Learning
• Deep Reinforcement Learning with Double Q-learning
• Dueling Network Architectures for Deep Reinforcement Learning
• Playing Atari with Deep Reinforcement Learning
• HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
• Deterministic Policy Gradient Algorithms
• Continuous control with deep reinforcement learning
• High-Dimensional Continuous Control Using Generalized Advantage Estimation
• Hybrid Reward Architecture for Reinforcement Learning
• Trust Region Policy Optimization
• Proximal Policy Optimization Algorithms
• Emergence of Locomotion Behaviours in Rich Environments
• Action-Conditional Video Prediction using Deep Networks in Atari Games
• A Distributional Perspective on Reinforcement Learning
• Distributional Reinforcement Learning with Quantile Regression
• The Option-Critic Architecture
• Some hyper-parameters are from DeepMind Control Suite, OpenAI Baselines and Ilya Kostrikov