Multiagent Reinforcement Learning (MARL) plays a pivotal role in intelligent vehicle systems, offering solutions for complex decision-making, coordination, and adaptive behavior among autonomous agents. This review aims to highlight the importance of fostering trust in MARL and emphasize the significance of MARL in revolutionizing intelligent vehicle systems. First, this paper summarizes the fundamental methods of MARL. Second, it identifies the limitations of MARL in safety, robustness, generalization, and ethical constraints and outlines the corresponding research methods. Then we summarize their applications in intelligent vehicle systems. Considering human interaction is essential to practical applications of MARL in various domains, the paper also analyzes the challenges associated with MARL's applications in human-machine systems. These challenges, when overcome, could significantly enhance the real-world implementation of MARL-based intelligent vehicle systems.
Multiagent Reinforcement Learning: Methods, Trustworthiness, Applications in Intelligent Vehicles, and Challenges
IEEE Transactions on Intelligent Vehicles ; 9 , 12 ; 8190-8211
2024-12-01
1747452 byte
Article (Journal)
Electronic Resource
English
A Social View to Multiagent Reinforcement Learning
British Library Online Contents | 2004
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