NDLI: Fuzzy Q-learning: a new approach for fuzzy dynamic programming

Content Provider	IEEE Xplore Digital Library
Author	Berenji, H.R.
Copyright Year	1994
Description	Author affiliation: Intelligent Interence Syst. Group, NASA Ames Res. Center, Mountain View, CA, USA (Berenji, H.R.)
Abstract	Fuzzy reinforcement learning (FRL) involves "jump starting" reinforcement learning with fuzzy logic rules. By using FRL, prior domain knowledge, which may be very approximate and imprecise, can be expressed in terms of fuzzy rules and refined later through the learning process. In this paper, we develop a new algorithm called fuzzy Q-learning (or FQ-Learning) which extends Watkin's Q-learning method. It can be used for decision processes in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. An example of a fuzzy constraint is: "the weight of object A must not be substantially heavier than w" where w is a specified weight. Similarly, an example of a fuzzy goal is: "the robot must be in the vicinity of door k". We show that FQ-learning provides an alternative solution to this problem which is simpler than the Bellman-Zadeh's fuzzy dynamic programming approach. We apply the algorithm to a multistage decision making problem and a navigation task.<>
Starting Page	486
Ending Page	491
File Size	479851
Page Count	6
File Format	PDF
ISBN	078031896X
DOI	10.1109/FUZZY.1994.343737
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-06-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dynamic programming Fuzzy systems Learning Decision making Intelligent systems Artificial intelligence Control systems Navigation NASA Fuzzy logic
Content Type	Text
Resource Type	Article

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