Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative MARL
文献类型:会议论文
作者 | Zhiwei Xu1,2![]() ![]() ![]() |
出版日期 | 2023 |
会议日期 | December 10-16, 2023 |
会议地点 | New Orleans, LA, USA |
英文摘要 | 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. |
语种 | 英语 |
URL标识 | 查看原文 |
源URL | [http://ir.ia.ac.cn/handle/173211/56538] ![]() |
专题 | 融合创新中心_决策指挥与体系智能 |
通讯作者 | Guoliang Fan |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 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]. 见:. New Orleans, LA, USA. December 10-16, 2023. |
入库方式: OAI收割
来源:自动化研究所
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