Viewing Classifier Systems as Model Free Learning in POMDPs

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Akira Hayashi, Nobuo Suematsu


Classifier systems are now viewed disappointing because of their prob(cid:173) lems such as the rule strength vs rule set performance problem and the credit assignment problem. In order to solve the problems, we have de(cid:173) veloped a hybrid classifier system: GLS (Generalization Learning Sys(cid:173) tem). 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.