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Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative MARL
Zhiwei Xu1,2; Bin Zhang1,2; Dapeng Li1,2; Guangchong Zhou1,2; Zeren Zhang1,2; Guoliang Fan1,2
2023
Conference NameAdvances in Neural Information Processing Systems
Conference DateDecember 10-16, 2023
Conference PlaceNew Orleans, LA, USA
Abstract

Value decomposition methods have gained popularity in the field of cooperative multi-agent reinforcement learning. However, almost all existing methods follow the principle of Individual Global Max (IGM) or its variants, which limits their problem-solving capabilities. To address this, we propose a dual self-awareness value decomposition framework, inspired by the notion of dual self-awareness in psychology, that entirely rejects the IGM premise. Each agent consists of an ego policy for action selection and an alter ego value function to solve the credit assignment problem. The value function factorization can ignore the IGM assumption by utilizing an explicit search procedure. On the basis of the above, we also suggest a novel anti-ego exploration mechanism to avoid the algorithm becoming stuck in a local optimum. As the first fully IGM-free value decomposition method, our proposed framework achieves desirable performance in various cooperative tasks.

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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
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56538
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,Dapeng Li,et al. Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative MARL[C],2023.
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