Electronics Optics & Control, Volume. 31, Issue 12, 91(2024)

An Air Defense Formation Deployment Method Based on Multi-Agent Reinforcement Learning

JIAN Zemin1... SHEN Guowei1, LIU Li2 and WANG Meiqi2 |Show fewer author(s)
Author Affiliations
  • 1State Key Laboratory of Public Big Data, College of Computer Science & Technology, Guizhou University, Guiyang 550000, China
  • 2JiangNan Design & Research Institute of Machinery & Electricity, Guiyang 550000, China
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    References(5)

    [7] [7] ZHAO W F, SUN H J, LI M, et al. Research on optimization of air defense firepower configuration for warship cooperating with strategic location on the sea[J]. Mathematical Problems in Engineering, 2023, 2023: 1-16.

    [9] [9] SUN W, CAO Z, WANG G, et al. An optimized double-nested anti-missile force deployment based on the deep Kuhn-Munkres algorithm[J]. Mathematics, 2022, 10(23): 4627.

    [11] [11] XUE N Y, DING D, DING J, et al. Optimization method for coordination deployment of air defense system based on improved genetic algorithm[C]//2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Chongqing: IEEE, 2021: 1658-1664.

    [13] [13] YU C, VELU A, VINITSKY E, et al. The surprising effectiveness of PPO in cooperative multi-agent games[C]//NIPS′22: Proceedings of the 36th International Conference on Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2022, 35: 24611-24624.

    [16] [16] SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[R]. Los Alamos: arXiv Preprint, 2017: arXiv: 1707.06347.

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    JIAN Zemin, SHEN Guowei, LIU Li, WANG Meiqi. An Air Defense Formation Deployment Method Based on Multi-Agent Reinforcement Learning[J]. Electronics Optics & Control, 2024, 31(12): 91

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    Paper Information

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    Received: Mar. 10, 2024

    Accepted: Dec. 25, 2024

    Published Online: Dec. 25, 2024

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2024.12.014

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