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FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks

文献类型：期刊论文

作者Zhang, Zhen1; Zhao, Dongbin2; Gao, Junwei1; Wang, Dongqing1; Dai, Yujie3
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2017-06-01
卷号47期号:6页码:1367-1379
关键词Multiagent Reinforcement Learning (Marl) Nash Equilibrium Q-learning Repeated Game
DOI10.1109/TCYB.2016.2544866
文献子类Article
英文摘要In 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.
WOS关键词EVOLUTIONARY GAME-THEORY ; TRAFFIC SIGNAL CONTROL ; NETWORKS ; APPROXIMATION ; DESIGN
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000401950400002
资助机构National Natural Science Foundation of China(61273136 ; Foundation of Shandong Province(ZR2015FM015 ; 61573353 ; ZR2015FM017) ; 61533017 ; 61573205)
源URL[http://ir.ia.ac.cn/handle/173211/15124]  
专题复杂系统管理与控制国家重点实验室_深度强化学习
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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