**Reference:**

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

Corresponding technical report: pdf file (1.03 MB)

@inproceedings{BusErn:07-008,

author={L. Bu{\c{s}}oniu and D. Ernst and B. {De 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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