Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving

ArXi:2501.08096v4 Announce Type: replace-cross Reinforcement Learning (RL) has shown excellent performance in solving decision-making and control problems of autonomous driving, which is increasingly applied in diverse driving scenarios. However, driving is a multi-attribute problem, leading to challenges in achieving multi-objective compatibility for current RL methods, especially in both policy updating and policy execution. On the one hand, a single value evaluation network limits the policy updating in complex scenarios with coupled driving objectives.