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<identifier>oai:HAL:hal-00520611v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Adapted Pittsburgh Classifier System: Applying Reinforcement Learning Techniques to Meteorological Forecasting</title>
<creator>Enée, Gilles</creator>
<creator>Peroumalnaïk, Mathias</creator>
<contributor>Groupe de Recherche en Informatique et Mathématiques Appliquées Antilles-Guyane (GRIMAAG) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>International Journal of Artificial Intelligence</source>
<identifier>hal-00520611</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00520611</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00520611</source>
<source>International Journal of Artificial Intelligence, 2008, 1 (A08), pp.96-110</source>
<language>en</language>
<subject>[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>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.</description>
<date>2008-09</date>
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