FUZZY REINFORCEMENT LEARNING

M. Andrecut () and M. K. Ali
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M. Andrecut: Department of Physics, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta, T1K 3M4, Canada
M. K. Ali: Department of Physics, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta, T1K 3M4, Canada

International Journal of Modern Physics C (IJMPC), 2002, vol. 13, issue 05, 659-674

Abstract: Fuzzy logic represents an extension of classical logic, giving modes of approximate reasoning in an environment of uncertainty and imprecision. Fuzzy inference systems incorporates human knowledge into their knowledge base on the conclusions of the fuzzy rules, which are affected by subjective decisions. In this paper we show how the reinforcement learning technique can be used to tune the conclusion part of a fuzzy inference system. The fuzzy reinforcement learning technique is illustrated using two examples: the cart centering problem and the autonomous navigation problem.

Keywords: Fuzzy logic; reinforcement learning (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1142/S0129183102003450

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