This paper explores the application of multi-agent reinforcement learning in collaborative observation path planning of marine environment observation network. In order to solve the problem of intelligent collaborative observation path in marine environment observation network, we improved the multi-agent reinforcement learning method based on value function. By decomposing the joint action-value functions into a single action-value functions, this method makes full use of the advantages of centralized training and completely decentralized implementation of the learned strategies. The data assimilation results show that the information collected from the path planned by this method can effectively improve the prediction accuracy of the marine environment numerical prediction system.


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    Titel :

    Multi-agent Reinforcement Learning for Cooperative Observation Path Planning of Ocean Mobile Observation Network


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Zhao, Yuxin (Autor:in) / Liu, Yanlong (Autor:in) / Du, Dengdui (Autor:in) / Cheng, Xiaohui (Autor:in) / Wang, Haiyang (Autor:in) / Deng, Xiong (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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