**Reference:**

L. Busoniu,
D. Ernst,
B. De Schutter, and
R. Babuska,
"Continuous-state reinforcement learning with fuzzy approximation," in
*Adaptive Agents and Multi-Agent Systems III. Adaptation and
Multi-Agent Learning* (K. Tuyls, A. Nowé, Z. Guessoum, and
D. Kudenko, eds.), vol. 4865 of *Lecture Notes in Computer
Science*, Berlin, Germany: Springer, ISBN 978-3-540-77947-6, pp.
27-43, 2008.

**Abstract:**

Reinforcement Learning (RL) is a widely used learning paradigm for
adaptive agents. There exist several convergent and consistent RL
algorithms which have been intensively studied. 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
architecture similar to those previously used for Q-learning, but we
combine it with the model-based Q-value iteration algorithm. We prove
that the resulting algorithm converges. We also give a modified,
asynchronous variant of the algorithm that converges at least as fast
as the original version. An illustrative simulation example is
provided.

Online version of the chapter

Corresponding technical report: pdf file (1.52 MB)

@incollection{BusErn:07-030,

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={Adaptive Agents and Multi-Agent Systems III.\ Adaptation and Multi-Agent Learning},

series={Lecture Notes in Computer Science},

volume={4865},

editor={K. Tuyls and A. Now\'e and Z. Guessoum and D. Kudenko},

publisher={Springer},

address={Berlin, Germany},

pages={27--43},

year={2008},

doi={10.1007/978-3-540-77949-0_3}

}

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