CASIA OpenIR  > 复杂系统认知与决策实验室  > 决策指挥与体系智能
Learning to Coordinate via Multiple Graph Neural Networks
Zhiwei Xu1,2; Bin Zhang1,2; Yunpeng Bai1,2; Dapeng Li1,2; Guoliang Fan1,2
2022
Conference NameInternational Conference on Neural Information Processing(ICONIP)
Pages52-63
Conference DateDecember 8-12, 2021
Conference PlaceBALI, Indonesia
Abstract

The collaboration between agents has gradually become an important topic in multi-agent systems. The key is how to efficiently solve the credit assignment problems. This paper introduces MGAN for collaborative multi-agent reinforcement learning, a new algorithm that combines graph convolutional networks and value-decomposition methods. MGAN learns the representation of agents from different perspectives through multiple graph networks, and realizes the proper allocation of attention between all agents. We show the amazing ability of the graph network in representation learning by visualizing the output of the graph network, and therefore improve interpretability for the actions of each agent in the multi-agent system.

DOI10.1007/978-3-030-92238-2\_5
URL查看原文
Indexed ByEI
Language英语
IS Representative Paper
Sub direction classification多智能体系统
planning direction of the national heavy laboratory多智能体决策
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Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56519
Collection复杂系统认知与决策实验室_决策指挥与体系智能
Corresponding AuthorGuoliang Fan
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhiwei Xu,Bin Zhang,Yunpeng Bai,et al. Learning to Coordinate via Multiple Graph Neural Networks[C],2022:52-63.
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