Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV control presents significant challenges, given the complexity of interconnectivity and coordination required among the vehicles. To address this, multi-agent reinforcement learning (MARL), with its notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, has emerged as a promising tool for enhancing the capabilities of CAVs. However, there is a notable absence of current reviews on the state-of-the-art MARL algorithms in the context of CAVs. Therefore, this paper delivers a comprehensive review of the application of MARL techniques within the field of CAV control. The paper begins by introducing MARL, followed by a detailed explanation of its unique advantages in addressing complex mobility and traffic scenarios that involve multiple agents. It then presents a comprehensive survey of MARL applications on the extent of control dimensions for CAVs, covering critical and typical scenarios such as platooning control, lane-changing, and unsignalized intersections. In addition, the paper provides a comprehensive review of the prominent simulation platforms used to create reliable environments for training in MARL. Lastly, the paper examines the current challenges associated with deploying MARL within CAV control and outlines potential solutions that can effectively overcome these issues. Through this review, the study highlights the tremendous potential of MARL to enhance the performance and collaboration of CAV control in terms of safety, travel efficiency, and economy.

翻译：互联自动驾驶车辆（CAVs）已成为应对未来安全、高效、环保交通系统挑战的潜在解决方案。然而，由于车辆间互联与协调所需的复杂性，CAV控制面临重大挑战。为此，多智能体强化学习（MARL）凭借其在自动驾驶、机器人技术及人车交互等复杂问题上的显著突破，已成为提升CAV能力的有力工具。然而，当前尚缺乏针对CAV场景下先进MARL算法的系统性综述。因此，本文对MARL技术在CAV控制领域的应用进行了全面综述。文章首先介绍MARL，随后详细阐述其在涉及多智能体的复杂移动与交通场景中的独特优势。接着，从CAV控制维度出发，涵盖编队控制、车道变换、无信号灯交叉口等关键典型场景，系统综述了MARL的应用现状。此外，本文全面梳理了用于构建可靠MARL训练环境的主流仿真平台。最后，文章剖析了将MARL部署于CAV控制所面临的当前挑战，并提出了有效克服这些问题的潜在解决方案。通过本综述，研究揭示了MARL在提升CAV控制安全性、行驶效率与经济性方面的巨大潜力。