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：网联自动驾驶车辆（CAVs）作为应对未来安全、高效、环保交通系统挑战的潜在解决方案，已崭露头角。然而，鉴于车辆间所需的互联与协调复杂性，CAV控制面临重大挑战。为此，多智能体强化学习（MARL）在解决自动驾驶、机器人技术及人车交互等复杂问题方面取得显著进展，已成为增强CAV能力的有力工具。然而，当前缺乏针对CAV领域最先进MARL算法的综述。因此，本文全面综述了MARL技术在CAV控制领域中的应用。论文首先介绍MARL，随后详细阐述其在解决涉及多智能体的复杂移动与交通场景中的独特优势。接着，围绕CAV控制维度，全面调研了MARL在关键典型场景中的应用，如队列控制、换道及无信号交叉口。此外，本文还系统评述了用于构建可靠MARL训练环境的代表性仿真平台。最后，论文探讨了当前MARL在CAV控制中部署所面临的挑战，并概述了有效克服这些问题的潜在解决方案。通过本综述，本文揭示了MARL在提升CAV控制安全性、出行效率与经济性方面的巨大潜力。