A Survey on Reinforcement Learning for Optimal Decision‐Making and Control of Intelligent Vehicles

ABSTRACT Reinforcement learning (RL) has been widely studied as an efficient class of machine learning methods for adaptive optimal control under uncertainties. In recent years, the applications of RL in optimised decision‐making and motion control of intelligent vehicles have received increasing attention. Due to the complex and dynamic operating environments of intelligent vehicles, it is necessary to improve the learning efficiency and generalisation ability of RL‐based decision and control algorithms under different conditions. This survey systematically examines the theoretical foundations, algorithmic advancements and practical challenges of applying RL to intelligent vehicle systems operating in complex and dynamic environments. The major algorithm frameworks of RL are first introduced, and the recent advances in RL‐based decision‐making and control of intelligent vehicles are overviewed. In addition to self‐learning decision and control approaches using state measurements, the developments of DRL methods for end‐to‐end driving control of intelligent vehicles are summarised. The open problems and directions for further research works are also discussed.