利用改进的 MADRL 进行多 UAV 协同机动决策以实现追击-规避

IF 5 Q1 ENGINEERING, MULTIDISCIPLINARY
Delin Luo , Zihao Fan , Ziyi Yang , Yang Xu
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引用次数: 0

摘要

针对多无人机追逐-规避对抗问题,提出了一种基于改进的多代理深度强化学习(MADRL)的无人机协同机动方法。在该方法中,基于通信机制的改进 CommNet 网络被引入到深度强化学习算法中,以解决多代理问题。在行动者网络结构中添加了一层门控递归单元(GRU),用于记忆历史环境状态。随后,在 CommNet 核心网络层中设计了另一个 GRU 作为通信通道,以完善无人机之间的通信信息。最后,给出了该算法在两组场景中的仿真结果,结果表明该方法具有良好的有效性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-UAV cooperative maneuver decision-making for pursuit-evasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning (MADRL) is proposed. In this method, an improved CommNet network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit (GRU) is added to the actor-network structure to remember historical environmental states. Subsequently, another GRU is designed as a communication channel in the CommNet core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

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来源期刊
Defence Technology(防务技术)
Defence Technology(防务技术) Mechanical Engineering, Control and Systems Engineering, Industrial and Manufacturing Engineering
CiteScore
8.70
自引率
0.00%
发文量
728
审稿时长
25 days
期刊介绍: Defence Technology, a peer reviewed journal, is published monthly and aims to become the best international academic exchange platform for the research related to defence technology. It publishes original research papers having direct bearing on defence, with a balanced coverage on analytical, experimental, numerical simulation and applied investigations. It covers various disciplines of science, technology and engineering.
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