This paper presents an autonomous decision-making framework for drone swarms using deep reinforcement learning, dividing UAVs into “reconnaissance” and “executive” roles. The framework includes a situational communication layer, task decision and planning layers, and a control layer. It employs a semi-centralized, semi-distributed mode in the communication layer and integrates Deep Deterministic Policy Gradient (DDPG) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in the decision and planning layers. An enhanced MADDPG algorithm is developed, resulting in a drone swarm autonomous decision-making method based on deep reinforcement learning. Simulations show that this approach outperforms in UAV swarm tasks.


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    Titel :

    Autonomous Decision-Making of Drone Swarm Based on Deep Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Yan, Liang (Herausgeber:in) / Duan, Haibin (Herausgeber:in) / Deng, Yimin (Herausgeber:in) / Zhang, Na (Autor:in) / Chen, Shuhan (Autor:in) / Xiong, Shixun (Autor:in) / Zhang, Fengying (Autor:in)

    Kongress:

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



    Erscheinungsdatum :

    04.03.2025


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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