Enhancing Multi-agent Coordination via Dual-channel Consensus
文献类型：期刊论文
| 作者 | Qingyang Zhang2,3 ; Kaishen Wang1,2; Jingqing Ruan2,3; Yiming Yang2; Dengpeng Xing1,2; Bo Xu1,2,3
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| 刊名 | Machine Intelligence Research
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| 出版日期 | 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. |
| 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.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, 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. |
入库方式： OAI收割
来源：自动化研究所
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