NDLI: Hybrid Q-learning algorithm about cooperation in MAS

Content Provider	IEEE Xplore Digital Library
Author	Wei Chen Jing Guo Xiong Li Jie Wang
Copyright Year	2009
Description	Author affiliation: Automation Faculty, GuangDong University of Technology, GuangZhou, 510006. China (Wei Chen; Jing Guo; Xiong Li; Jie Wang)
Abstract	In most cases, agent learning tends to be a good method for solving challenging problems in multi-agent System (MAS). Since the learning efficiency is significantly different according to the actions taken by each specific agent, suitable algorithms will play important roles in the answer of the mentioned problems in multi-agent system. Although many related work are addressed to different algorithms of agent learning, few of them could balance efficiency and accuracy. In this paper, a hybrid Q-learning algorithm named CE-NNR which is springed form the CE-Q learning and NNR Q-learning is presented. The algorithm is then well extended to RoboCup soccer simulation system and is proved to be reasonable with the experimental results arranged at the end of this paper.
Starting Page	3943
Ending Page	3947
File Size	167404
Page Count	5
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5191990
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion Multiagent systems Automation Educational robots Humanoid robots Intelligent robots RoboCup 2D Soccer Simulation Parallel robots Robot kinematics CE-NNR Q-Learning Optimal control Artificial intelligence MAS
Content Type	Text
Resource Type	Article

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