Offline Goal-Conditioned Reinforcement Learning for Safety-Critical Tasks with Recovery Policy

arXiv:2403.01734v1 Announce Type: cross
Abstract: Offline goal-conditioned reinforcement learning (GCRL) aims at solving goal-reaching tasks with sparse rewards from an offline dataset. While prior work has demonstrated various approaches for agents to learn near-optimal policies, these methods encounter limitations when dealing with diverse constraints in complex environments, such as safety constraints. Some of these approaches prioritize goal attainment without considering safety, while others excessively focus on safety at the expense of training efficiency. In this paper, we study the problem …

arxiv cs.ai cs.lg cs.ro offline policy recovery reinforcement reinforcement learning safety safety-critical tasks type