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Clique-based cooperative multiagent reinforcement learning using factor graphs
Zhang,Zhen1; Zhao DB(赵冬斌)2
Source PublicationIEEE/CAA Journal of Automatica Sinica
In this paper, we propose a clique-based sparse reinforcement learning (RL) algorithm for solving cooperative tasks. The aim is to accelerate the learning speed of the original sparse RL algorithm and to make it applicable for tasks decomposed in a more general manner. First, a transition function is estimated and used to update the Q-value function, which greatly reduces the learning time. Second, it is more reasonable to divide agents into cliques, each of which is only responsible for a specific subtask. In this way, the global Q-value function is decomposed into the sum of several simpler local Q-value functions. Such decomposition is expressed by a factor graph and exploited by the general maxplus algorithm to obtain the greedy joint action. Experimental results show that the proposed approach outperforms others with better performance.
KeywordReinforcement Learning Factor Graphs
Document Type期刊论文
Affiliation1.Department of Electric Engineering, College of Automation Engineering, Qingdao University
2.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China
Recommended Citation
GB/T 7714
Zhang,Zhen,Zhao DB. Clique-based cooperative multiagent reinforcement learning using factor graphs[J]. IEEE/CAA Journal of Automatica Sinica,2015,3(1):248-256.
APA Zhang,Zhen,&Zhao DB.(2015).Clique-based cooperative multiagent reinforcement learning using factor graphs.IEEE/CAA Journal of Automatica Sinica,3(1),248-256.
MLA Zhang,Zhen,et al."Clique-based cooperative multiagent reinforcement learning using factor graphs".IEEE/CAA Journal of Automatica Sinica 3.1(2015):248-256.
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