title: Using the XCS classifier system for multi-objective reinforcement learning problems.
abstract: We investigate the performance of a learning classifier system in some simple multi-objective, multi-step maze problems, using both random and biased action-selection policies for exploration. Results show that the choice of action-selection policy can significantly affect the performance of the system in such environments. Further, this effect is directly related to population size, and we relate this finding to recent theoretical studies of learning classifier systems in single-step problems.
authors.affiliation: Faculty of Computing, Engineering and Mathematics, University of West of England, Bristol BS16 1QY, UK. matthew2.studley@uwe.ac.uk
authors.forename: Matthew
authors.initials: M
authors.lastname: Studley
authors.forename: Larry
authors.initials: L
authors.lastname: Bull
mesh_terms: D001185:Artificial Intelligence; D006040:Goals; D007858:Learning; D012054:Reinforcement, Psychology