NDLI: Implementation of fuzzy Q-learning based on modular fuzzy model and parallel structured learning

Content Provider	IEEE Xplore Digital Library
Author	Watanabe, T.
Copyright Year	2009
Description	Author affiliation: Faculty of Engineering, Osaka Electro-Communication University, Neyagawa, Osaka, Japan (Watanabe, T.)
Abstract	In order to realize intelligent agent such as autonomous mobile robots, Reinforcement Learning is one of the necessary techniques in control system. Fuzzy Q-learning is one of the promising approaches for implementation of reinforcement learning function owing to its high ability of model representation. However, in applying fuzzy Q-learning to actual application, the number of iterations for learning also becomes huge as well as almost all Q-learning application. Furthermore convergence performance is often deteriorated owing to its complicated model structure. In this study, implementation method of fuzzy Q-learning is discussed in order to improve the learning performance of fuzzy Q-learning. The modular fuzzy model construction method based on fuzzy Q-learning is proposed in this paper. Multi-grain configuration of modular fuzzy model is compared with parallel structured learning scheme. Through numerical experiments of mountain car task and Acrobot task, I found that the proposed construction of modular fuzzy model improved the performance of fuzzy Q-learning.
Starting Page	1338
Ending Page	1344
File Size	237029
Page Count	7
File Format	PDF
ISBN	9781424427932
ISSN	1062922X
DOI	10.1109/ICSMC.2009.5346250
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Fuzzy systems Modular construction Humans Intelligent agent Mobile robots Convergence Fuzzy reasoning Cybernetics USA Councils mountain car task reinforcement learning Q-learning fuzzy Q-learning modular fuzzy model Acrobot
Content Type	Text
Resource Type	Article

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