An Adaptive Strategy via Reinforcement Learning for the Prisoner's Dilemma Game

查看全文 作　　者：Lei [1]Xue;Changyin [1,5]Sun;Donald [2,5]Wunsch;Yingjiang [3]Zhou;Fang [4]Yu

机构地区：[1]Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China;[2]Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA;[3]College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;[4]Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 210096, China;[5]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》索引2018年第5卷第1期,共10页

基　　金：supported by the National Natural Science Foundation(NNSF)of China(61603196,61503079,61520106009,61533008);the Natural Science Foundation of Jiangsu Province of China(BK20150851);China Postdoctoral Science Foundation(2015M581842);Jiangsu Postdoctoral Science Foundation(1601259C);Nanjing University of Posts and Telecommunications Science Foundation(NUPTSF)(NY215011);Priority Academic Program Development of Jiangsu Higher Education Institutions,the open fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering,Ministry of Education(MCCSE2015B02);the Research Innovation Program for College Graduates of Jiangsu Province(CXLX1309) 摘　　要：The iterated prisoner's dilemma(IPD) is an ideal model for analyzing interactions between agents in complex networks. It has attracted wide interest in the development of novel strategies since the success of tit-for-tat in Axelrod's tournament. This paper studies a new adaptive strategy of IPD in different complex networks, where agents can learn and adapt their strategies through reinforcement learning method. A temporal difference learning method is applied for designing the adaptive strategy to optimize the decision making process of the agents. Previous studies indicated that mutual cooperation is hard to emerge in the IPD. Therefore, three examples which based on square lattice network and scale-free network are provided to show two features of the adaptive strategy. First, the mutual cooperation can be achieved by the group with adaptive agents under scale-free network, and once evolution has converged mutual cooperation, it is unlikely to shift. Secondly, the adaptive strategy can earn a better payoff compared with other strategies in the square network. The analytical properties are discussed for verifying evolutionary stability of the adaptive strategy. 关键词：Complex network prisoner’s dilemma reinforcement learning temporal differences learning

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An Adaptive Strategy via Reinforcement Learning for the Prisoner's Dilemma Game

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