Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving

arXiv:2311.10747v3 Announce Type: replace-cross
Abstract: In the domain of autonomous driving, the offline Reinforcement Learning~(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline datasets. However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets. In this paper, we introduce the saFety-aware strUctured Scenario representatION (FUSION), a pioneering representation learning method in offline RL to facilitate the learning of a generalizable end-to-end driving policy by leveraging structured …

arxiv autonomous autonomous driving causal cs.ai cs.lg cs.ro driving offline reinforcement reinforcement learning representation safety trustworthy type