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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
| Date | Stars |
|---|---|
| 2026-08-04 | 578 |
| 2026-08-06 | 578 |
| 2026-08-07 | 577 |
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| 2026-09-13 | 582 |
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| 2026-09-28 | 583 |
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| 2026-10-24 | 594 |
| 2026-10-25 | 595 |
| 2026-10-26 | 596 |
| 2026-11-03 | 598 |
| 2026-11-04 | 600 |
Today
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Momentum
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<div align="center">
<h1>
Safety-Gymnasium
</h1>
</div>
<div align="center">
<a></a>
<a href="https://pypi.org/project/safety-gymnasium"></a>
<a href="https://safety-gymnasium.readthedocs.io"></a>
<a href="https://pepy.tech/project/safety-gymnasium"></a>
<a href="https://github.com/PKU-Alignment/safety-gymnasium/stargazers"></a>
<a href="https://github.com/PKU-Alignment/safety-gymnasium/blob/HEAD/LICENSE"></a>
</div>
<p align="center">
<a href="https://github.com/PKU-Alignment/safety-gymnasium#why-safety-gymnasium">Why Safety-Gymnasium?</a> |
<a href="https://www.safety-gymnasium.com">Documentation</a> |
<a href="https://github.com/PKU-Alignment/safety-gymnasium#installation">Install guide</a> |
<a href="https://github.com/PKU-Alignment/safety-gymnasium#customize-your-environments">Customization</a> | <a href="https://sites.google.com/view/safety-gymnasium">Video</a>
</p>
Safety-Gymnasium is a highly scalable and customizable Safe Reinforcement Learning (SafeRL) library.
It aims to deliver a good view of benchmarking SafeRL algorithms and a standardized set of environments.
We provide a set of standard APIs which are compatible with information on constraints.
Users can explore new insights via an elegant code framework and well-designed environments.
## Citing Safety-Gymnasium
If you find Safety-Gymnasium useful, please cite it in your publications.
```bibtex
@inproceedings{ji2023safety,
title={Safety Gymnasium: A Unified Safe Reinforcement Learning Benchmark},
author={Jiaming Ji and Borong Zhang and Jiayi Zhou and Xuehai Pan and Weidong Huang and Ruiyang Sun and Yiran Geng and Yifan Zhong and Josef Dai and Yaodong Yang},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and BencExcerpt of 29,903 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1d654197e4493a1b, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning
