Multi-agent system (MAS) has become a distributed artificial intelligence (DAI) research hotspot, and has gained wide-ranging research in robotics, artificial intelligence, and knowledge acquisition. The RoboCup Rescue Simulation (RCRS) system is a typical MAS platform for studying multi-agent coordination. In the RCRS, the rescue agent faces an unknown disaster environment, real-time dynamic changes, and limited resources for communication. This paper focuses on the cooperative task planning problem of multiple fire protection agents based on reinforcement learning, and uses the simulation platform to conduct experiments using the Proximal Policy Optimization (PPO) algorithm. Multiple fire-fighting agents can learn cooperative fire-fighting strategy well after training for certain episodes.


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    Titel :

    Multi-agent Task Coordination Method Based on RCRS


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Xueke, Yang (Autor:in) / Yu, Zhang (Autor:in) / Junren, Luo (Autor:in) / Kaiqiang, Wang (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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