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ISSN
1064-5462
E-ISSN
1530-9185
2014 Impact factor:
1.39

Artificial Life

Winter 2007, Vol. 13, No. 1, Pages 69-86
(doi: 10.1162/artl.2007.13.1.69)
© 2006 Massachusetts Institute of Technology
Using the XCS Classifier System for Multi-objective Reinforcement Learning Problems
Article PDF (252.74 KB)
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.