GLS : a Hybrid Classifier System Based on POMDP Research

Abstract

Classifier systems are now viewed disappointing because of their problems such as the rule strength vs rule set performance problem and the credit assignment problem. In order to solve the problems, we have developed a hybrid classifier system: GLS (Generalization Learning System). In designing GLS, we view CSs as model free learning in POMDPs and take a hybrid approach to finding the best generalization, given the total number of rules. GLS uses the policy improvement procedure by Jackal et al. for the optimal stochastic policy when a set of rule conditions is given. GLS uses GA to search for the best set of rule conditions.