ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL.

ChainerRL is tested with 3.6. For other requirements, see requirements.txt.

ChainerRL can be installed via PyPI:

pip install chainerrl

It can also be installed from the source code:

python setup.py install

Refer to Installation for more information on installation.

You can try ChainerRL Quickstart Guide first, or check the examples ready for Atari 2600 and Open AI Gym.

For more information, you can refer to ChainerRL's documentation.

Following algorithms have been implemented in ChainerRL:

• A2C (Synchronous variant of A3C)
  • examples: [atari (batched)] [general gym (batched)]
• A3C (Asynchronous Advantage Actor-Critic)
  • examples: [atari reproduction] [atari] [general gym]
• ACER (Actor-Critic with Experience Replay)
  • examples: [atari] [general gym]
• Asynchronous N-step Q-learning
  • examples: [atari]
• Categorical DQN
  • examples: [atari] [general gym]
• DQN (Deep Q-Network) (including Double DQN, Persistent Advantage Learning (PAL), Double PAL, Dynamic Policy Programming (DPP))
  • examples: [atari reproduction] [atari] [atari (batched)] [flickering atari] [general gym]
• DDPG (Deep Deterministic Policy Gradients) (including SVG(0))
  • examples: [mujoco reproduction] [mujoco] [mujoco (batched)]
• IQN (Implicit Quantile Networks)
  • examples: [atari reproduction] [general gym]
• PCL (Path Consistency Learning)
  • examples: [general gym]
• PPO (Proximal Policy Optimization)
  • examples: [mujoco reproduction] [atari] [mujoco] [mujoco (batched)]
• Rainbow
  • examples: [atari reproduction]
• REINFORCE
  • examples: [general gym]
• SAC (Soft Actor-Critic)
  • examples: [mujoco reproduction]
• TRPO (Trust Region Policy Optimization) with GAE (Generalized Advantage Estimation)
  • examples: [mujoco]
• TD3 (Twin Delayed Deep Deterministic policy gradient algorithm)
  • examples: [mujoco reproduction]

Following useful techniques have been also implemented in ChainerRL:

• NoisyNet
  • examples: [Rainbow] [DQN/DoubleDQN/PAL]
• Prioritized Experience Replay
  • examples: [Rainbow] [DQN/DoubleDQN/PAL]
• Dueling Network
  • examples: [Rainbow] [DQN/DoubleDQN/PAL]
• Normalized Advantage Function
  • examples: [DQN] (for continuous-action envs only)
• Deep Recurrent Q-Network
  • examples: [DQN]

ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI.

Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used.

Any kind of contribution to ChainerRL would be highly appreciated! If you are interested in contributing to ChainerRL, please read CONTRIBUTING.md.

MIT License.

To cite ChainerRL in publications, please cite our JMLR paper:

@article{JMLR:v22:20-376,
  author  = {Yasuhiro Fujita and Prabhat Nagarajan and Toshiki Kataoka and Takahiro Ishikawa},
  title   = {ChainerRL: A Deep Reinforcement Learning Library},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {77},
  pages   = {1-14},
  url     = {http://jmlr.org/papers/v22/20-376.html}
}