Reference
L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška, "Fuzzy
partition optimization for approximate fuzzy Q-iteration,"
Proceedings of the 17th IFAC World Congress, Seoul, Korea,
pp. 5629-5634, July 2008.
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.
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BibTeX
@inproceedings{BusErn:07-035,
author = {Bu{\c{s}}oniu, Lucian and Ernst, Damien and De Schutter, Bart
and Babu{\v{s}}ka, Robert},
title = {Fuzzy Partition Optimization for Approximate Fuzzy
{Q}-Iteration},
booktitle = {Proceedings of the 17th IFAC World Congress},
address = {Seoul, Korea},
pages = {5629--5634},
month = jul,
year = {2008}
}