[2501.08096] Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving

Computer Science > Robotics arXiv:2501.08096 (cs) [Submitted on 14 Jan 2025 (v1), last revised 28 Mar 2026 (this version, v4)] Title:Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving Authors:Guizhe Jin, Zhuoren Li, Bo Leng, Wei Han, Lu Xiong, Chen Sun View a PDF of the paper titled Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving, by Guizhe Jin and 5 other authors View PDF HTML (experimental) Abstract: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. On the other hand, the common single-type action space structure limits driving flexibility or results in large behavior fluctuations during policy execution. To this end, we propose a Multi-objective Ensemble-Critic reinforcement learning method with Hybrid Parametrized Action for multi-objective compatible autonomous driving. Specifically, an advanced MORL architecture is constructed, in which the ensemble-critic focuses on different objectives through independent reward functions. ...