Cognition-Driven Multiagent Policy Learning Framework for Promoting Cooperation
文献类型：期刊论文
| 作者 | Pu, Zhiqiang1,3 ; Wang, Huimu2; Liu, Boyin1,3; Yi, Jianqiang1,3
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| 刊名 | IEEE TRANSACTIONS ON GAMES
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 15期号:3页码:388-398 |
| ISSN号 | 2475-1502 |
| 关键词 | Cognition difference coupling cognition network (CCN) deep reinforcement learning (DRL) graph convolutional network multiagent systems (MASs) |
| DOI | 10.1109/TG.2022.3186386 |
| 通讯作者 | Pu, Zhiqiang(zhiqiang.pu@ia.ac.cn) |
| 英文摘要 | Many attempts have been made to promote cooperation for multiagent systems. However, several issues that draw less attentions but may dramatically degrade the cooperation performance still exist, such as redundant information interactions among neighbors, and difficulties in understanding complex and dynamic environments from high-level cognition. To address these limitations, a cognition-driven multiagent policy (CDMAP) learning framework is proposed in this article. It includes a cognition difference network (CDN), a coupling cognition network (CCN), and a policy optimization network (PON). CDN is designed based on a variational autoencoder, where a concept of cognition difference is defined to prune redundant interactions among agents for more efficient communication. Based on the pruned topology, CCN captures the hidden representations of the surrounding environment. Several coupling graph attention layers are incorporated in CCN, each layer with different but coupling adjacent matrices, yielding a comprehensive state understanding from multiple representation spaces. Based on the captured hidden states, PON generates the final policies, where QMIX is adopted as a value factorization method to alleviate the credit-assignment problem. At last, CDMAP is evaluated through two representative multiagent games including Google Research Football and StarCraft II. The results demonstrate its superior effectiveness compared with existing methods. |
| WOS关键词 | GAME |
| 资助项目 | National Key Research Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences[2020AAA0103404] ; External Cooperation Key Project of Chinese Academy Sciences[XDA27030204] ; Science and Technology Development Fund of Macau[173211KYSB20200002] ; [0025/2019/AKP] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001068900800007 |
| 资助机构 | National Key Research Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; External Cooperation Key Project of Chinese Academy Sciences ; Science and Technology Development Fund of Macau |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53059]  |
| 专题 | 复杂系统认知与决策实验室 |
| 通讯作者 | Pu, Zhiqiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.JD COM, Beijing 100176, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Pu, Zhiqiang,Wang, Huimu,Liu, Boyin,et al. Cognition-Driven Multiagent Policy Learning Framework for Promoting Cooperation[J]. IEEE TRANSACTIONS ON GAMES,2023,15(3):388-398. |
| APA | Pu, Zhiqiang,Wang, Huimu,Liu, Boyin,&Yi, Jianqiang.(2023).Cognition-Driven Multiagent Policy Learning Framework for Promoting Cooperation.IEEE TRANSACTIONS ON GAMES,15(3),388-398. |
| MLA | Pu, Zhiqiang,et al."Cognition-Driven Multiagent Policy Learning Framework for Promoting Cooperation".IEEE TRANSACTIONS ON GAMES 15.3(2023):388-398. |
入库方式： OAI收割
来源：自动化研究所
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