**Reference:**

L. Busoniu,
D. Ernst,
B. De Schutter, and
R. Babuska,
"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.

Online version of the paper

Corresponding technical report: pdf file (225 KB)

@inproceedings{BusErn:07-035,

author={L. Bu{\c{s}}oniu and D. Ernst and B. {D}e Schutter and R. Babu{\v{s}}ka},

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},

doi={10.3182/20080706-5-KR-1001.00949}

}

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