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	Enhancing Multi-agent Coordination via Dual-channel Consensus
	Qingyang Zhang1,3; Kaishen Wang1,2; Jingqing Ruan1,3; Yiming Yang1; Dengpeng Xing1,2; Bo Xu1,2,3
刊名	Machine Intelligence Research
	2024
卷号	21期号:2页码:349-368
关键词	Multi-agent reinforcement learning, contrastive representation learning, consensus, multi-agent cooperation, cognitive consistency
ISSN号	2731-538X
DOI	10.1007/s11633-023-1464-2
英文摘要	Successful coordination in multi-agent systems requires agents to achieve consensus. Previous works propose methods through information sharing, such as explicit information sharing via communication protocols or exchanging information implicitly via behavior prediction. However, these methods may fail in the absence of communication channels or due to biased modeling. In this work, we propose to develop dual-channel consensus (DuCC) via contrastive representation learning for fully cooperative multi-agent systems, which does not need explicit communication and avoids biased modeling. DuCC comprises two types of consensus: temporally extended consensus within each agent (inner-agent consensus) and mutual consensus across agents (inter-agent consensus). To achieve DuCC, we design two objectives to learn representations of slow environmental features for inner-agent consensus and to realize cognitive consistency as inter-agent consensus. Our DuCC is highly general and can be flexibly combined with various MARL algorithms. The extensive experiments on StarCraft multi-agent challenge and Google research football demonstrate that our method efficiently reaches consensus and performs superiorly to state-of-the-art MARL algorithms.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/56043]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
作者单位	1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3.School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
推荐引用方式 GB/T 7714	Qingyang Zhang,Kaishen Wang,Jingqing Ruan,et al. Enhancing Multi-agent Coordination via Dual-channel Consensus[J]. Machine Intelligence Research,2024,21(2):349-368.
APA	Qingyang Zhang,Kaishen Wang,Jingqing Ruan,Yiming Yang,Dengpeng Xing,&Bo Xu.(2024).Enhancing Multi-agent Coordination via Dual-channel Consensus.Machine Intelligence Research,21(2),349-368.
MLA	Qingyang Zhang,et al."Enhancing Multi-agent Coordination via Dual-channel Consensus".Machine Intelligence Research 21.2(2024):349-368.

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