The thinking communication network with semi-multiple communication cycles under the multi-agent deep reinforcement learning
ZOU Qi-jie
TANG Yu
GAO Bing
ZHAO Xi-ling
ZHANG Zhe-jie
Abstract：To address the problem of single communication content and sparse information in multi-agent systems under a cooperative environment,this paper proposes a thinking multi-agent communication network(TMACN)based on deep reinforcement learning of multi-agent.Firstly,the agent considers the differences of different information sources in the interaction process,and the agent fuses the received communication information with their own historical experience information to form inference information,and use this information as a new sent message,so as to achieve the goal of improving the diversity of communication contents.Then,the model designs a semi-multi-round communication strategy based on the soft attention mechanism,which improves the information saturation and thus enhances the communication interaction efficiency of the system.This paper demonstrates that TMACN improves the accuracy and stability of the system compared to other methods in three simulated environments:cooperative navigation,hunting task and traffic junction.
Keywords：multi-agent systemscooperative environmentdeep reinforcement learningcommunication network
Publication Date：2025-03-31
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 553-562 )