Intelligent connected vehicles (ICVs) platoon can be abstracted as a multi-agent system (MAS), and the cooperative adaptive cruise control (CACC) for it has long been a research hot spot. In this paper, a multi-agent reinforcement learning (MARL) with knowledge transfer approach has been developed for solving CACC. Based on the advanced successor feature, the knowledge transfer from source MASs is supposed to enable the control strategy to fully understand the cooperative relationships within various heterogeneous MASs, which is also proved to help the algorithm achieve jump-start improvement. Experimental results demonstrate the effectiveness of the MARL-based CACC solution.


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    Title :

    Exploring Heterogeneous Multi-agent Reinforcement Learning with Knowledge Transfer for CACC of ICVs Platoon


    Contributors:
    Yan, Jiapeng (author) / Li, Qiyue (author) / Li, Yuanqing (author) / Zheng, Zhenxing (author) / Hu, Huimin (author) / Li, Jiaojiao (author)


    Publication date :

    2024-10-25


    Size :

    1013940 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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