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2021 JMLR JMLR 2021

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2021. (edit, beta)

🌉 Interdisciplinary Bridge — Machine Learning and Reinforcement Learning
🧭 Keyword Pioneer — reinforcement learning library
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy

Authors

Antonin Raffin , Ashley Hill , Adam Gleave , Anssi Kanervisto , Maximilian Ernestus , Noah Dormann

Topics

Machine Learning > Application Areas > Efficient Computing Reinforcement Learning > Methods > Deep RL

Keywords

deep reinforcement learning python implementation reinforcement learning library reproducible experiment algorithm benchmark
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