Continuous-State Reinforcement Learning with Fuzzy Approximation
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.
Downloads
- Corresponding technical report:
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Bibtex entry
@inproceedings{BusErn:07-008,
author={L. Bu{\c{s}}oniu and D. Ernst and B. {D}e Schutter and R.
Babu{\v{s}}ka},
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={K. Tuyls and S. {de Jong} and M. Ponsen and K. Verbeeck},
address={Maastricht, The Netherlands},
pages={21--35},
month=apr,
year={2007}
}
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