Stable-Baselines3 Docs - Reliable Reinforcement Learning Implementations

Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines.

Github repository: https://github.com/DLR-RM/stable-baselines3

Paper: https://jmlr.org/papers/volume22/20-1364/20-1364.pdf

RL Baselines3 Zoo (training framework for SB3): https://github.com/DLR-RM/rl-baselines3-zoo

RL Baselines3 Zoo provides a collection of pre-trained agents, scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.

SB3 Contrib (experimental RL code, latest algorithms): https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

Main Features

  • Unified structure for all algorithms

  • PEP8 compliant (unified code style)

  • Documented functions and classes

  • Tests, high code coverage and type hints

  • Clean code

  • Tensorboard support

  • The performance of each algorithm was tested (see Results section in their respective page)

User Guide

RL Algorithms

Common

Misc

Citing Stable Baselines3

To cite this project in publications:

@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}
}

Contributing

To any interested in making the rl baselines better, there are still some improvements that need to be done. You can check issues in the repo.

If you want to contribute, please read CONTRIBUTING.md first.

Indices and tables