The interception game between groups of unmanned aerial vehicles (UAVs) is crucial in the future intelligent warfare. In response to the collaborative interception gaming problem against aerial cluster attacks, a multi-agent deep reinforcement learning (DRL) framework based on the twin delayed deep deterministic policy gradient (TD3) method is proposed. The framework combines single-agent delayed policy gradient algorithms with a centralized evaluation and distributed execution algorithm architecture. In order to enhance the convergence of the algorithm, a generalized advantage function is designed. The simulation results show that the strategy enables UAVs to assign interception targets based on real-time battlefield conditions intelligently.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Multi-agent Reinforcement Learning Framework for Coordinated Multi-UAV Interception Strategies


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Deng, Yimin (Herausgeber:in) / Chen, Hong (Autor:in) / Li, Bochen (Autor:in) / Wang, Chenggang (Autor:in) / Ding, Lu (Autor:in) / Song, Lei (Autor:in)

    Kongress:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Erscheinungsdatum :

    06.03.2025


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Multi-Agent Coordinated Interception of Multiple Rogue Drones

    Valianti, Panayiota / Papaioannou, Savvas / Kolios, Panayiotis et al. | IEEE | 2020


    Multi-Agent Reinforcement Learning for Multiple Rogue Drone Interception

    Valianti, Panayiota / Malialis, Kleanthis / Kolios, Panayiotis et al. | IEEE | 2023



    Coordinated Multi-Robot Exploration using Reinforcement Learning

    Mete, Atharva / Mouhoub, Malek / Farid, Ali Moltajaei | IEEE | 2023


    Autonomous Drone Interception with Reinforcement Learning

    Gauffriau, Adrien / Grasset, Damien / Bertoin, David et al. | TIBKAT | 2022

    Freier Zugriff