NDLI: Multi-agent Q-learning with joint state value approximation

Content Provider	IEEE Xplore Digital Library
Author	Chen Gang Cao Weihua Chen Xin Wu Min
Copyright Year	2011
Description	Author affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China (Chen Gang; Cao Weihua; Chen Xin; Wu Min)
Abstract	This paper relieves the “curse of dimensionality” problem, which becomes intractable when scaling reinforcement learning to multi-agent systems. This problem is aggravated exponentially as the number of agents increases, resulting in large memory requirement and slowness in learning speed. For cooperative systems which are widely existed in multi-agent systems, this paper proposes a new multi-agent Q-learning algorithm based on the decomposing the joint state and joint action learning into two learning processes, which are learning individual action and the maximum value of the joint state approximately. The latter process considers others' actions to insure the joint action is optimal and supports the updating of the former one. The simulation results illustrate that the proposed algorithm can learn the optimal joint behavior with smaller memory and faster speed comparing with Friend-Q learning.
Starting Page	4878
Ending Page	4882
File Size	311582
Page Count	5
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Learning systems Learning Multiagent systems Q-learning Curse of dimensionality Multi-agent system Memory management Games Cooperative systems Markov processes Decomposition Joints
Content Type	Text
Resource Type	Article

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