Reference
L. Buşoniu, D. Ernst, B. De Schutter, and R. Babuška,
"Continuous-state reinforcement learning with fuzzy approximation,"
Proceedings of the 7th Annual Symposium on Adaptive and Learning
Agents and Multi-Agent Systems (ALAMAS 2007) (K. Tuyls, S. de Jong, M.
Ponsen, and K. Verbeeck, eds.), Maastricht, The Netherlands, pp. 21-35, Apr.
2007.
Abstract
Reinforcement learning (RL) is a widely used learning paradigm for adaptive
agents. Well-understood RL algorithms with good convergence and consistency
properties exist. In their original form, these algorithms require that the
environment states and agent actions take values in a relatively small discrete
set. Fuzzy representations for approximate, model-free RL have been proposed in
the literature for the more difficult case where the state-action space is
continuous. In this work, we propose a fuzzy approximation structure similar to
those previously used for Q-learning, but we combine it with the model-based
Q-value iteration algorithm. We show that the resulting algorithm converges. We
also give a modified, serial variant of the algorithm that converges at least
as fast as the original version. An illustrative simulation example is
provided.
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BibTeX
@inproceedings{BusErn:07-008,
author = {Bu{\c{s}}oniu, Lucian and Ernst, Damien and De Schutter, Bart
and Babu{\v{s}}ka, Robert},
title = {Continuous-State Reinforcement Learning with Fuzzy
Approximation},
booktitle = {Proceedings of the 7th Annual Symposium on Adaptive and
Learning Agents and Multi-Agent Systems (ALAMAS 2007)},
editor = {Tuyls, Karl and de Jong, Steven and Ponsen, Maarten and
Verbeeck, Katja},
address = {Maastricht, The Netherlands},
pages = {21--35},
month = apr,
year = {2007}
}