【摘要】 轨迹跟踪避撞是车辆智能性的重要体现，针对现有控制方法面对同一场景的控制风格单一问题，本文中提出了一种多风格型强化学习控制方法。为实现控制风格多样性，首次将风格指标引入值网络和策略网络，搭建了多风格跟踪避撞策略网络，并结合值分布强化学习理论构建了多风格策略迭代框架，依托该框架推导提出了多风格值分布强化学习算法。仿真和实车试验表明：所提出方法可以多种驾驶风格（激进、中性、保守）完成轨迹跟踪避撞任务，实车稳态轨迹跟踪误差小于5 cm，具备较高的控制精度，实车平均单步决策耗时仅为6.07 ms，满足实时性要求。更多还原

【Abstract】 Trajectory tracking and collision avoidance are key functions of vehicle intelligence. For the singular control style limitation of existing control methods in the same scene, a novel multi-style reinforcement learning(RL) method is proposed in this paper. To achieve diversity in control styles, style indicators are innovatively incorporated into value and policy networks to establish a multi-style tracking and collision avoidance policy network.Alongside this, a multi-style policy iteration framework is developed combining the distributional RL theory. Based on the framework, a multi-style distributional soft actor-critic algorithm(M-DSAC) is put forward. Through simulation and real vehicle tests, it is validated that the proposed method is capable of executing trajectory tracking and collision avoidance tasks across various driving styles, such as aggressive, neutral, and conservative, with the real vehicle’s steady-state trajectory tracking error less than 5 cm, with high control accuracy. The average single-step decision-making time for the real vehicle is merely 6.07 ms, meeting real-time requirements.更多还原