Aircraft cluster air combat scenario is a long sequence decision-making task with complex state change, difficult control, which the use of general supervised learning, RNN network method is difficult to deal with, considering the excellent performance of multi-agent reinforcement learning in solving energy distribution, team games and other aspects in recent years, we established a set of six-degree-of-freedom aircraft simulation core of the 2v2 air combat scenario, proposed a multi-agent deep reinforcement learning agent based on the air combat conditions. The work introduced the basic concepts of deep reinforcement learning and the situation assessment involved in air combat, and trained a 6-degree-of-freedom aircraft model according to the Markov process of multiple agents. The final results show that the model generated by the training can generate a basic joint confrontation strategy, which has high research value and practical significance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    2v2 Air Combat Confrontation Strategy Based on Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Fu, Wenxing (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Wang, Jinlin (Autor:in) / Zhu, Longtao (Autor:in) / Yang, Hongyu (Autor:in) / Ji, Yulong (Autor:in) / Wang, Xiaoming (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2022 ; Xi'an, China September 23, 2022 - September 25, 2022



    Erscheinungsdatum :

    10.03.2023


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Decision-making and confrontation in close-range air combat based on reinforcement learning

    YANG, Mengchao / SHAN, Shengzhe / ZHANG, Weiwei | Elsevier | 2025

    Freier Zugriff


    UAV Swarm Confrontation Using Hierarchical Multiagent Reinforcement Learning

    Baolai Wang / Shengang Li / Xianzhong Gao et al. | DOAJ | 2021

    Freier Zugriff

    Target Allocation Algorithm for Two UAV Air Combat Confrontation

    Luyu, Wang / Xusheng, Gan / Nan, Wu et al. | IEEE | 2023