This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases. Our benchmark suite contains three packages: 1) expertly crafted safe policies, 2) D4RL-styled datasets along with environment wrappers, and 3) high-quality offline safe RL baseline implementations. We feature a methodical data collection pipeline powered by advanced safe RL algorithms, which facilitates the generation of diverse datasets across 38 popular safe RL tasks, from robot control to autonomous driving. We further introduce an array of data post-processing filters, capable of modifying each dataset's diversity, thereby simulating various data collection conditions. Additionally, we provide elegant and extensible implementations of prevalent offline safe RL algorithms to accelerate research in this area. Through extensive experiments with over 50000 CPU and 800 GPU hours of computations, we evaluate and compare the performance of these baseline algorithms on the collected datasets, offering insights into their strengths, limitations, and potential areas of improvement. Our benchmarking framework serves as a valuable resource for researchers and practitioners, facilitating the development of more robust and reliable offline safe RL solutions in safety-critical applications. The benchmark website is available at \url{www.offline-saferl.org}.

翻译：本文提出了一套专为离线安全强化学习挑战设计的综合基准测试套件，旨在促进训练与部署阶段安全学习算法的开发与评估。我们的基准套件包含三个模块：1）专家设计的离线安全策略，2）基于D4RL风格的数据集及环境封装器，3）高质量的离线安全强化学习基线实现。我们利用先进安全强化学习算法构建了系统化的数据采集流程，可在38个主流安全强化学习任务（涵盖机器人控制到自动驾驶领域）中生成多样化数据集。进一步引入系列数据后处理过滤器，通过调整各数据集的多样性来模拟不同数据采集条件。此外，我们提供了主流离线安全强化学习算法的优雅可扩展实现，以加速该领域研究。通过超50000 CPU小时与800 GPU小时的大规模实验，我们评估并比较了这些基线算法在采集数据集上的性能，揭示了其优势、局限及潜在改进方向。本基准测试框架为研究人员与从业者提供了宝贵资源，助力开发更鲁棒可靠的离线安全强化学习解决方案，适用于安全关键型应用。基准测试网站访问地址为\url{www.offline-saferl.org}。