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Type of publication:Inproceedings
Entered by:LB
TitleFuzzy Partition Optimization for Approximate Fuzzy Q-iteration
Bibtex cite IDBusoniu-ifac08
Booktitle Proceedings of the 17th IFAC World Congress (IFAC 2008)
Year published 2008
Month July
Pages 5629-5634
Location 6-11 July 2008, Seoul, Korea
Keywords reinforcement learning,approximate reinforcement learning,fuzzy approximation,adaptive basis functions,cross-entropy optimization
Abstract
Abstract: Reinforcement learning (RL) is a widely used learning paradigm for adaptive agents. Because exact RL can only be applied to very simple problems, approximate algorithms are usually necessary in practice. Many algorithms for approximate RL rely on basis-function representations of the value function (or of the Q-function). Designing a good set of basis functions without any prior knowledge of the value function (or of the Q-function) can be a difficult task. In this paper, we propose instead a technique to optimize the shape of a constant number of basis functions for the approximate, fuzzy Q-iteration algorithm. In contrast to other approaches to adapt basis functions for RL, our optimization criterion measures the actual performance of the computed policies in the task, using simulation from a representative set of initial states. A complete algorithm, using cross-entropy optimization of triangular fuzzy membership functions, is given and applied to the car-on-the-hill example.
Authors
Busoniu, Lucian
Ernst, Damien
De Schutter, Bart
Babuška, Robert
Topics
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