Simulation of Reinforcement Learning-based UAV Swarm Adversarial Strategy Deduction

doi:10.12382/bgxb.2023.0877

Abstract

Abstract:

The application of drone clusters in military warfare, public safety, and commercial fields is becoming increasingly widespread. But it is a challenge to develop the efficient strategiesin complex and ever-changing adversarial environments. In order to enable the drone clusters to autonomously learn and adapt to the change in adversarial environment, and improve the efficiency and success rate of task execution, a multi-agent reinforcement learning algorithm framework based on value decomposition is proposed. The behavior of drone clusters in different adversarial scenarios is simulated on a simulation platform, and the ability of drone clusters to make decisions in different situations is cultivated to achieve the optimal task objectives through reinforcement learning algorithms. The application and performance comparison of different reinforcement learning algorithms in drone swarm adversarial strategies are discussed. The experimental results show that the proposed algorithm shows good performance in various cluster confrontation environments, demonstrating its strong support in military drone cluster confrontation.

Key words: drone swarm, adversarial strategy, deep reinforcement learning, value decomposition

CLC Number:

TP18
E91

CAO Zijian, SUN Zelong, YAN Guochuang, FU Yanfang, YANG Bo, LI Qinjie, LEI Kailin, GAO Linghang. Simulation of Reinforcement Learning-based UAV Swarm Adversarial Strategy Deduction[J]. Acta Armamentarii, 2023, 44(S2): 126-134.

Figures/Tables 9

References 32

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智能体	状态空间
红方智能体	经度、纬度、高度、速度、朝向、燃油、损伤、弹药量
蓝芳智能体	经度、纬度、高度、速度、朝向、燃油、损伤、弹药量

智能体	状态空间
红方智能体	经度、纬度、高度、速度、朝向、燃油、损伤、弹药量
蓝芳智能体	经度、纬度、高度、速度、朝向、燃油、损伤、弹药量

智能体	动作空间
红方智能体	方位、朝向角、攻击、高度、速度
蓝方智能体	方位、朝向角、高度、速度

智能体	动作空间
红方智能体	方位、朝向角、攻击、高度、速度
蓝方智能体	方位、朝向角、高度、速度