Stable-Baselines3: Reliable Reinforcement Learning Implementations
Abstract
Stable-Baselines3 provides open-source implementations of deep reinforcement learning (RL) algorithms in Python. The implementations have been benchmarked against reference codebases, and automated unit tests cover 95% of the code. The algorithms follow a consistent interface and are accompanied by extensive documentation, making it simple to train and compare different RL algorithms. Our documentation, examples, and source-code are available at https://github.com/DLR-RM/stable-baselines3.
Cite
Text
Raffin et al. "Stable-Baselines3: Reliable Reinforcement Learning Implementations." Machine Learning Open Source Software, 2021.Markdown
[Raffin et al. "Stable-Baselines3: Reliable Reinforcement Learning Implementations." Machine Learning Open Source Software, 2021.](https://mlanthology.org/mloss/2021/raffin2021jmlr-stablebaselines3/)BibTeX
@article{raffin2021jmlr-stablebaselines3,
title = {{Stable-Baselines3: Reliable Reinforcement Learning Implementations}},
author = {Raffin, Antonin and Hill, Ashley and Gleave, Adam and Kanervisto, Anssi and Ernestus, Maximilian and Dormann, Noah},
journal = {Machine Learning Open Source Software},
year = {2021},
pages = {1-8},
volume = {22},
url = {https://mlanthology.org/mloss/2021/raffin2021jmlr-stablebaselines3/}
}