防务技术2024,Issue(1) :544-556.DOI:10.1016/j.dt.2022.07.005

Targeted multi-agent communication algorithm based on state control

Li-yang Zhao Tian-qing Chang Lei Zhang Jie Zhang Kai-xuan Chu De-peng Kong
防务技术2024,Issue(1) :544-556.DOI:10.1016/j.dt.2022.07.005

Targeted multi-agent communication algorithm based on state control

Li-yang Zhao 1Tian-qing Chang 1Lei Zhang 1Jie Zhang 1Kai-xuan Chu 1De-peng Kong2
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作者信息

  • 1. Department of Weaponry and Control,Army Academy of Armored Forces,Beijing,100072,China
  • 2. Unit 92942,Beijing,100161,China
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Abstract

As an important mechanism in multi-agent interaction,communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents.However,the existing communication schemes can bring much timing redundancy and irrelevant messages,which seriously affects their practical application.To solve this problem,this paper proposes a targeted multi-agent communication algorithm based on state control(SCTC).The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard-and self-attention mechanisms,realizing targeted communication message processing.In addition,by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message,the correctness of the final action choice of the agent is ensured.Our evaluation using a challenging set of StarCraft Ⅱ benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents,thus ensuring better cooperation between agents.

Key words

Multi-agent deep reinforcement learning/State control/Targeted interaction/Communication mechanism

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出版年

2024
防务技术
中国兵工学会

防务技术

CSTPCD
影响因子:0.358
ISSN:2214-9147
参考文献量40
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