Adapted Pittsburgh classifier system: building accurate strategies in non markovian environments

This paper focuses on the study of the behavior of a genetic algorithm based classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on maze type environments containing aliasing squares. This type of environment is often used in reinforcement learning litterature to assess the performances of learning methods when facing problems containing non markov situations. Through this study, we discuss on the performances of the APCS on maze type environments and also of the efficiency of an improvement of the APCS learning method inspired from the XCS : the covering mechanism. We manage to show that, without any memory mechanism, the APCS is able to build and to keep accurate strategies to produce regular suboptimal solutions to these maze problem. This statement is shown through a comparison of the results obtained by the XCS, XCSM and XCSMH on distinct maze problems with these obtained by the APCS. Categories and Subject Descriptors I.2.6 [Learning]: [Concept learning, kno...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Gilles Énée, Mathias Péroumalnaïk
• Keywords: APCS Learning Method, Classifier Systems, GECCO 2008, Maze Type Environments, Optimization