Cost-aware Offline Safe Meta Reinforcement Learning with Robust In-Distribution Online Task Adaptation

Despite the gained prominence made by reinforcement learning (RL) in various domains, ensuring safety in real-world applications remains a significant challenge. Offline safe RL, which learns safe policies from pre-collected data, has emerged to address these concerns. However, existing approaches assume a single constraint mode and lack adaptability to diverse safety constraints. In realworld scenarios, we often find ourselves working with datasets gathered from various tasks, with the aim of developing a generalized policy capable of handling unknown tasks during testing. To deal with such offline safe meta RL problem, we introduce a novel framework called COSTA, which is designed to facilitate the learning of a safe generalized policy that can adapt and be transferred