Viewing Classifier Systems as Model Free Learning in POMDPs
Akira Hayashi, Nobuo Suematsu · 1998
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 Jaakkola et al. for an locally optimal stochastic policy when a set of rule conditions is given. GLS uses GA to search for the best set of rule conditions. 1 INTRODUCTION Classifier systems (CSs) (Holland 1986) have been among the most used in reinforcement learning. Some of the advantages of CSs are (1) they have a built-in feature (the use of don't care symbols "#") for input generalization, and (2) the complexity of policies can be controlled by restricting the number of rules. In spite of these attract...