Learning classifier systems from a reinforcement learning perspective |
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Authors: | P L Lanzi |
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Affiliation: | (1) Dipartimento di Elettronica ed Informazione, Politecnico di Milano, Piazza Leonardo da Vinci n. 32, 1-20133 Milano e-mail: pierluca.lanzi@polimi.it, IT |
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Abstract: | We analyze learning classifier systems in the light of tabular reinforcement learning. We note that although genetic algorithms
are the most distinctive feature of learning classifier systems, it is not clear whether genetic algorithms are important
to learning classifiers systems. In fact, there are models which are strongly based on evolutionary computation (e.g., Wilson's
XCS) and others which do not exploit evolutionary computation at all (e.g., Stolzmann's ACS). To find some clarifications,
we try to develop learning classifier systems “from scratch”, i.e., starting from one of the most known reinforcement learning
technique, Q-learning. We first consider thebasics of reinforcement learning: a problem modeled as a Markov decision process
and tabular Q-learning. We introduce a formal framework to define a general purpose rule-based representation which we use
to implement tabular Q-learning. We formally define generalization within rules and discuss the possible approaches to extend
our rule-based Q-learning with generalization capabilities. We suggest that genetic algorithms are probably the most general
approach for adding generalization although they might be not the only solution. |
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Keywords: | Genetic algorithms Reinforcement learning XCS Q-learning |
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