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.


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    Title :

    Multiagent Reinforcement Learning: Methods, Trustworthiness, Applications in Intelligent Vehicles, and Challenges


    Contributors:
    Zhou, Ziyuan (author) / Liu, Guanjun (author) / Tang, Ying (author)

    Published in:

    Publication date :

    2024-12-01


    Size :

    1747452 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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