Learning classifier systems from a reinforcement learning perspective
type: Item
instance of: scholarly article
title: Learning classifier systems from a reinforcement learning perspective
zbMATH Open document ID: 1027.68664
published in: Soft Computing
publication date: 2003-03-18T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68U99
zbMATH DE Number: 1883899
zbMATH Keywords: learning classifier systems
zbMATH Keywords: tabular reinforcement learning
author: Pier Luca Lanzi
MaRDI profile type: Publication
full work available at URL: s005000100113
OpenAlex ID: W2073677619
Wikidata QID: Q56442540
DOI: 10.1007/S005000100113
DBLP publication ID: journals/soco/Lanzi02
description: scientific article; zbMATH DE number 1883899