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性基准测试套件，旨在促进训练与部署阶段安全学习算法的发展与评估。我们的基准套件包含三个模块：1）专家构建的安全策略，2）遵循D4RL标准格式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。我们基于先进的安全强化学习算法构建了系统化的数据采集流程，可生成覆盖机器人控制到自动驾驶等38个常见安全强化学习任务的多样化数据集。进一步引入一系列数据后处理过滤器，能够调整各数据集的多样性，从而模拟不同的数据采集条件。此外，我们提供了主流离线安全强化学习算法的优雅且可扩展的实现，以加速该领域的研究。通过累计超过5万CPU小时与800GPU小时的计算实验，我们在所采集的数据集上评估并比较了基线算法的性能，揭示了其优势、局限及潜在改进方向。本基准框架为研究人员与实践者提供了宝贵资源，有助于在安全关键应用中开发更鲁棒可靠的离线安全强化学习解决方案。基准测试网站见\url{www.offline-saferl.org}。