Neurocomputing: foundations of research
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Classifiers that approximate functions
Natural Computing: an international journal
Classifier fitness based on accuracy
Evolutionary Computation
Classifier prediction based on tile coding
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension
Evolutionary Computation
A Learning Classifier System Approach to Relational Reinforcement Learning
Learning Classifier Systems
Evolving Classifiers Ensembles with Heterogeneous Predictors
Learning Classifier Systems
Self organizing classifiers and niched fitness
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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XCSF extends the typical concept of learning classifier systems through the introduction of computed classifier prediction. Initial results show that XCSF's computed prediction can be used to evolve accurate piecewise linear approximations of simple functions. In this paper, we take XCSF one step further and apply it to typical reinforcement learning problems involving delayed rewards. In essence, we use XCSF as a method of generalized (linear) reinforcement learning to evolve piecewise linear approximations of the payoff surfaces of typical multistep problems. Our results show that XCSF can easily evolve optimal and near optimal solutions for problems introduced in the literature to test linear reinforcement learning methods.