Artificial intelligence (AI) systems possess significant potential to drive societal progress. However, their deployment often faces obstacles due to substantial safety concerns. Safe reinforcement learning (SafeRL) emerges as a solution to optimize policies while simultaneously adhering to multiple constraints, thereby addressing the challenge of integrating reinforcement learning in safety-critical scenarios. In this paper, we present an environment suite called Safety-Gymnasium, which encompasses safety-critical tasks in both single and multi-agent scenarios, accepting vector and vision-only input. Additionally, we offer a library of algorithms named Safe Policy Optimization (SafePO), comprising 16 state-of-the-art SafeRL algorithms. This comprehensive library can serve as a validation tool for the research community. By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications. The website of this project can be accessed at https://sites.google.com/view/safety-gymnasium.

翻译：人工智能（AI）系统具备推动社会进步的巨大潜力，然而其部署常因重大的安全性问题而受阻。安全强化学习（SafeRL）作为一种解决方案，能够在优化策略的同时满足多重约束，从而应对在安全关键场景中整合强化学习的挑战。本文提出了一套名为Safety-Gymnasium的环境套件，该套件涵盖了单智能体和多智能体场景中的安全关键任务，支持向量和纯视觉输入。此外，我们还提供了一组名为安全策略优化（SafePO）的算法库，包含16种最先进的安全强化学习算法。这一综合性算法库可作为研究社区的验证工具。通过引入该基准，我们旨在促进安全性性能的评估与比较，进而推动强化学习在更安全、更可靠且负责任的现实应用中的发展。本项目网站可通过https://sites.google.com/view/safety-gymnasium 访问。