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Adapted Pittsburgh Classifier System: Applying Reinforcement Learning Techniques to Meteorological Forecasting



This paper focuses on the study of the behaviour of a singular classifier system, the Adapted Pittsburgh Classifier System (A.P.C.S), on environments containing aliasing situations. Maze type environments are often used in reinforcement learning literature to assess the performances of learning methods when facing problems containing non Markovian situations. Those situations are often encountered when performing reinforcement learning on aliased data samples issued from meteorological simulations. Through this study, we discuss on the performances of the APCS on two mazes (Woods 101 and Maze E2) and also of the efficiency of an improvement of the APCS learning method inspired from the X Classifier System (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 solution to those maze problems. By extending this statement, we discuss on the usability of the prediction ability of this classifier system when used in a forecasting purpose on meteorological and geographical data.

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