Open AccessMulti-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Author(s) -
Min Hua,
Xinda Qi,
Dong Chen,
Kun Jiang,
Zemin Eitan Liu,
Hongyu Sun,
Quan Zhou,
Hongming Xu
Publication year - 2025
Publication title -
ieee transactions on automation science and engineering
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.314
H-Index - 87
eISSN - 1558-3783
pISSN - 1545-5955
DOI - 10.1109/tase.2025.3574280
Subject(s) - robotics and control systems , power, energy and industry applications , components, circuits, devices and systems
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 significant challenges due to the complexity of interconnectivity and co-ordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this paper offers a comprehensive review of MARL’s application in CAV control. The paper begins with an introduction to MARL, explaining its unique advantages in handling complex and multi-agent scenarios. It then presents a detailed survey of MARL applications across various control dimensions for CAVs, including critical scenarios such as platooning control, lane-changing, and unsignalized intersections. Additionally, the paper reviews prominent simulation platforms essential for developing and testing MARL algorithms for CAVs. Lastly, it examines the current challenges in deploying MARL for CAV control, including safety, communication, mixed traffic, and sim-to-real challenges. Potential solutions discussed include hierarchical MARL, decentralized MARL, adaptive interactions, and offline MARL. The work has been summarized in MARL_in_CAV_Control_Repository.