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FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks
Zhang, Zhen1; Zhao, Dongbin2; Gao, Junwei1; Wang, Dongqing1; Dai, Yujie3
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
2017-06-01
Volume47Issue:6Pages:1367-1379
SubtypeArticle
AbstractIn this paper, we propose a multiagent reinforcement learning algorithm dealing with fully cooperative tasks. The algorithm is called frequency of the maximum reward Q-learning (FMRQ). FMRQ aims to achieve one of the optimal Nash equilibria so as to optimize the performance index in multiagent systems. The frequency of obtaining the highest global immediate reward instead of immediate reward is used as the reinforcement signal. With FMRQ each agent does not need the observation of the other agents' actions and only shares its state and reward at each step. We validate FMRQ through case studies of repeated games: four cases of two-player two-action and one case of three-player two-action. It is demonstrated that FMRQ can converge to one of the optimal Nash equilibria in these cases. Moreover, comparison experiments on tasks with multiple states and finite steps are conducted. One is box-pushing and the other one is distributed sensor network problem. Experimental results show that the proposed algorithm outperforms others with higher performance.
KeywordMultiagent Reinforcement Learning (Marl) Nash Equilibrium Q-learning Repeated Game
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TCYB.2016.2544866
WOS KeywordEVOLUTIONARY GAME-THEORY ; TRAFFIC SIGNAL CONTROL ; NETWORKS ; APPROXIMATION ; DESIGN
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61273136 ; Foundation of Shandong Province(ZR2015FM015 ; 61573353 ; ZR2015FM017) ; 61533017 ; 61573205)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000401950400002
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15124
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Affiliation1.Qingdao Univ, Coll Automat Engn, Qingdao 266071, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.China Acad Railway Sci, Transportat & Econ Inst, Beijing 100081, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Zhen,Zhao, Dongbin,Gao, Junwei,et al. FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(6):1367-1379.
APA Zhang, Zhen,Zhao, Dongbin,Gao, Junwei,Wang, Dongqing,&Dai, Yujie.(2017).FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks.IEEE TRANSACTIONS ON CYBERNETICS,47(6),1367-1379.
MLA Zhang, Zhen,et al."FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks".IEEE TRANSACTIONS ON CYBERNETICS 47.6(2017):1367-1379.
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