ChainerRL 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.
ChainerRL is tested with Python 2.7+ and 3.5.1+. 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.
| Algorithm | Discrete Action | Continous Action | Recurrent Model | CPU Async Training |
|---|---|---|---|---|
| DQN (including DoubleDQN etc.) | ✓ | ✓ (NAF) | ✓ | x |
| DDPG | x | ✓ | ✓ | x |
| A3C | ✓ | ✓ | ✓ | ✓ |
| ACER | ✓ | ✓ | ✓ | ✓ |
| NSQ (N-step Q-learning) | ✓ | ✓ (NAF) | ✓ | ✓ |
| PCL (Path Consistency Learning) | ✓ | ✓ | ✓ | ✓ |
Following algorithms have been implemented in ChainerRL:
- A3C (Asynchronous Advantage Actor-Critic)
- ACER (Actor-Critic with Experience Replay)
- Asynchronous N-step Q-learning
- DQN (including Double DQN, Persistent Advantage Learning (PAL), Double PAL, Dynamic Policy Programming (DPP))
- DDPG (Deep Deterministic Poilcy Gradients) (including SVG(0))
- PGT (Policy Gradient Theorem)
- PCL (Path Consistency Learning)
- PPO (Proximal Policy Optimization)
Q-function based algorithms such as DQN can utilize a Normalized Advantage Function (NAF) to tackle continuous-action problems as well as DQN-like discrete output networks.
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.