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A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents

文献类型：期刊论文

作者Zhang, Zhen1; Wang, Dongqing1; Zhao, Dongbin2,3; Han, Qiaoni1; Song, Tingting1,4
刊名IEEE ACCESS
出版日期2018
卷号6页码:70223-70235
关键词Multi-agent reinforcement learning gradient ascent Q-learning cooperative tasks
ISSN号2169-3536
DOI10.1109/ACCESS.2018.2878853
通讯作者Zhang, Zhen(tbsunshine8@163.com)
英文摘要Multi-agent reinforcement learning (MARL) can be used to design intelligent agents for solving cooperative tasks. Within the MARL category, this paper proposes the probability of maximal reward based on the infinitesimal gradient ascent (PMR-IGA) algorithm to reach the maximal total reward in repeated games. Theoretical analyses show that in a finite-player-finite-action repeated game with two pure optimal joint actions where no common component action exists, both the optimal joint actions are stable critical points of the PMR-IGA model. Furthermore, we apply the Q-value function to estimate the gradient and derive the probability of maximal reward based on estimated gradient ascent (PMR-EGA) algorithm. Theoretical analyses and simulations of case studies of repeated games show that the maximal total reward can be achieved under any initial conditions. The PMR-EGA can be naturally extended to optimize cooperative stochastic games. Two stochastic games, i.e., box pushing and a distributed sensor network, are used as test beds. The simulations show that the PMR-EGA displays consistently an excellent performance for both stochastic games.
WOS关键词EVOLUTIONARY GAME-THEORY ; POLICY GRADIENT ; SYSTEMS
资助项目Shandong Provincial Natural Science Foundation of China[ZR2017PF005] ; National Natural Science Foundation of China[61873138] ; National Natural Science Foundation of China[61803218] ; National Natural Science Foundation of China[61573353] ; National Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[61573205]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000453261200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Shandong Provincial Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/25665]  
专题复杂系统管理与控制国家重点实验室_深度强化学习
通讯作者Zhang, Zhen
作者单位1.Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Qingdao Metro Grp Co Ltd, Operating Branch, Qingdao 266000, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Zhen,Wang, Dongqing,Zhao, Dongbin,et al. A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents[J]. IEEE ACCESS,2018,6:70223-70235.
APA Zhang, Zhen,Wang, Dongqing,Zhao, Dongbin,Han, Qiaoni,&Song, Tingting.(2018).A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents.IEEE ACCESS,6,70223-70235.
MLA Zhang, Zhen,et al."A Gradient-Based Reinforcement Learning Algorithm for Multiple Cooperative Agents".IEEE ACCESS 6(2018):70223-70235.

入库方式： OAI收割

来源：自动化研究所

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