  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}.
