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 can be installed via PyPI:
pip install chainerrl
It can also be installed from the source code:
python setup.py install
You can try ChainerRL Quickstart Guide first, or check the examples ready for Atari 2600 and Open AI Gym.
| Algorithm | Discrete Action | Continous Action | Recurrent Model | CPU Async Training |
|---|---|---|---|---|
| DQN (including DoubleDQN etc.) | o | o (NAF) | o | x |
| DDPG | x | o | o | x |
| A3C | o | o | o | o |
| ACER | o | x | o | o |
| NSQ (N-step Q-learning) | o | o (NAF) | o | o |
Following algorithms have been implemented in ChainerRL:
- A3C (Asynchronous Advantage Actor-Critic)
- ACER (Actor-Critic with Experience Replay) (only the discrete-action version for now)
- 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)
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.
To test chainerrl modules, install nose and run nosetests.
To test examples, run test_examples.sh.
Any kind of contribution to ChainerRL would be highly appreciated! If you are interested in contributing to ChainerRL, please read CONTRIBUTING.md.