FMRQ-A Multiagent Reinforcement Learning Algorithm for Fully Cooperative Tasks
Zhang, Zhen1; Zhao, Dongbin2; Gao, Junwei1; Wang, Dongqing1; Dai, Yujie3
2017-06-01
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
卷号47期号:6页码:1367-1379
文章类型Article
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.
关键词Multiagent Reinforcement Learning (Marl) Nash Equilibrium Q-learning Repeated Game
WOS标题词Science & Technology ; Technology
DOI10.1109/TCYB.2016.2544866
关键词[WOS]EVOLUTIONARY GAME-THEORY ; TRAFFIC SIGNAL CONTROL ; NETWORKS ; APPROXIMATION ; DESIGN
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61273136 ; Foundation of Shandong Province(ZR2015FM015 ; 61573353 ; ZR2015FM017) ; 61533017 ; 61573205)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000401950400002
引用统计
文献类型期刊论文
条目标识符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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Zhang, Zhen]的文章
[Zhao, Dongbin]的文章
[Gao, Junwei]的文章
百度学术
百度学术中相似的文章
[Zhang, Zhen]的文章
[Zhao, Dongbin]的文章
[Gao, Junwei]的文章
必应学术
必应学术中相似的文章
[Zhang, Zhen]的文章
[Zhao, Dongbin]的文章
[Gao, Junwei]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。