In this article, a GRU-Multi-agent Proximal Policy Optimization (GRU-MAPPO) algorithm was proposed to address unmanned aerial vehicle (UAV) cooperative air combat decision-making problem. This algorithm adds a layer of GRU to the Actor-Critic network framework, uses update gate to extract the historical temporal information and enhance situational awareness. Finally, experiments in our constructed UAV cooperative air combat environment demonstrate that UAVs using the algorithm proposed in this article can learn effective strategies in air combat environments and achieve high win rates.


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    Titel :

    UAV Cooperative Air Combat Maneuvering Decision-Making Using GRU-MAPPO


    Beteiligte:
    Chen, Caiyi (Autor:in) / Guo, Zhengyu (Autor:in) / Luo, Delin (Autor:in) / Xu, Yang (Autor:in) / Duan, Haibin (Autor:in)


    Erscheinungsdatum :

    18.06.2024


    Format / Umfang :

    845517 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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