**Reference:**

L. Busoniu,
D. Ernst,
B. De Schutter, and
R. Babuska,
"Fuzzy approximation for convergent model-based reinforcement
learning," *Proceedings of the 2007 IEEE International Conference
on Fuzzy Systems (FUZZ-IEEE 2007)*, London, UK, pp. 968-973, July
2007.

**Abstract:**

Reinforcement Learning (RL) is a learning control paradigm that
provides well-understood algorithms with good convergence and
consistency properties. Unfortunately, these algorithms require that
process states and control actions take only discrete values.
Approximate solutions using fuzzy representations have been proposed
in the literature for the case when the states and possibly the
actions are continuous. However, the link between these mainly
heuristic solutions and the larger body of work on approximate RL,
including convergence results, has not been made explicit. In this
paper, we propose a fuzzy approximation structure for the Q-value
iteration algorithm, and show that the resulting algorithm is
convergent. The proof is based on an extension of previous results in
approximate RL. We then propose a modified, serial version of the
algorithm that is guaranteed to converge at least as fast as the
original algorithm. An illustrative simulation example is also
provided.

Corresponding technical report: pdf file (166 KB)

@inproceedings{BusErn:07-011,

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

title={Fuzzy approximation for convergent model-based reinforcement learning},

booktitle={Proceedings of the 2007 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007)},

address={London, UK},

pages={968--973},

month=jul,

year={2007}

}

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