Datasets and Benchmarks for Offline Safe Reinforcement Learning
This work addresses the need for reproducible and robust evaluation in offline safe RL, which is crucial for safety-critical applications like robot control and autonomous driving, though it is incremental as it builds on existing methods and datasets.
The paper tackles the lack of standardized evaluation for offline safe reinforcement learning by introducing a comprehensive benchmarking suite with datasets, baselines, and tools across 38 tasks, and it provides extensive experimental results from over 50000 CPU and 800 GPU hours of computation to compare algorithm performance.
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}.