A survey of reinforcement learning theories in autonomous vehicles

This survey explores the core theories, techniques, and core applications of reinforcement learning (RL) in the domain of autonomous vehicles. RL has emerged as a significant contributor to decision-making techniques in this field, enabling agents to make informed decisions based on actions and rewards derived from the environment. The ultimate objective of RL in autonomous driving is to maximize cumulative rewards over time. However, RL faces challenges related to interpretability and sample efficiency, particularly in complex driving scenarios. This survey extensively investigates the utilization of RL in decision-making and control, encompassing various scenarios and addressing the challenges encountered within RL-based autonomous vehicles. By emphasizing the design of effective reward functions, enhancing sample efficiency, and improving model interpretability, future advancements in reinforcement learning for autonomous vehicles can foster the development of more robust, efficient, and trustworthy autonomous systems. Moreover, this survey provides valuable insights into the limitations of RL techniques in autonomous driving decision-making, highlighting areas that require further research and development.