Enhancing learning capabilities by XCS with best action mapping

Authors:
Masaya Nakata;Pier Luca Lanzi;Keiki Takadama
Affiliations:
Department of Informatics, The university of Electo-Communications, Tokyo, Japan;Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy;Department of Informatics, The university of Electo-Communications, Tokyo, Japan
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Year:
2012

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Abstract

This paper proposes a novel approach of XCS called XCS with Best Action Mapping (XCSB) to enhance the learning capabilities of XCS. The feature of XCSB is to learn only best actions having the highest predicted payoff with the high accuracy unlike XCS which learns actions having the highest and lowest predicted payoff with the high accuracy. To investigate the effectiveness of XCSB, we applied XCSB to two benchmark problems: multiplexer problem as a single step problem and maze problem as a multi step problem. The experimental results show that (1) XCSB can solve quickly the problem which has a large state space and (2) XCSB can achieve a high performance with a small max population size.