Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code. "sb3-contrib" for short.
A place for RL algorithms and tools that are considered experimental, e.g. implementations of the latest publications. Goal is to keep the simplicity, documentation and style of stable-baselines3 but for less matured implementations.
Over the span of stable-baselines and stable-baselines3, the community has been eager to contribute in form of better logging utilities, environment wrappers, extended support (e.g. different action spaces) and learning algorithms.
However sometimes these utilities were too niche to be considered for stable-baselines or proved to be too difficult to integrate well into the existing code without creating a mess. sb3-contrib aims to fix this by not requiring the neatest code integration with existing code and not setting limits on what is too niche: almost everything remotely useful goes! We hope this allows us to provide reliable implementations following stable-baselines usual standards (consistent style, documentation, etc) beyond the relatively small scope of utilities in the main repository.
See documentation for the full list of included features.
RL Algorithms:
- Truncated Quantile Critics (TQC)
- Quantile Regression DQN (QR-DQN)
- PPO with invalid action masking (MaskablePPO)
- Trust Region Policy Optimization (TRPO)
- Augmented Random Search (ARS)
Gym Wrappers:
Documentation is available online: https://sb3-contrib.readthedocs.io/
To install Stable Baselines3 contrib with pip, execute:
pip install sb3-contrib
We recommend to use the master version of Stable Baselines3.
To install Stable Baselines3 master version:
pip install git+https://github.com/DLR-RM/stable-baselines3
To install Stable Baselines3 contrib master version:
pip install git+https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
If you want to contribute, please read CONTRIBUTING.md guide first.
To cite this repository in publications (please cite SB3 directly):
@article{stable-baselines3,
author = {Antonin Raffin and Ashley Hill and Adam Gleave and Anssi Kanervisto and Maximilian Ernestus and Noah Dormann},
title = {Stable-Baselines3: Reliable Reinforcement Learning Implementations},
journal = {Journal of Machine Learning Research},
year = {2021},
volume = {22},
number = {268},
pages = {1-8},
url = {http://jmlr.org/papers/v22/20-1364.html}
}