Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Cognitive systems based on adaptive algorithms
ACM SIGART Bulletin
Use of infeasible individuals in probabilistic model building genetic network programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Recent advances in rule-based systems, i.e., Learning Classifier Systems (LCSs), have shown their sequential decision-making ability with a generalization property. In this paper, a novel LCS named eXtended rule-based Genetic Network Programming (XrGNP) is proposed. Different from most of the current LCSs, the rules are represented and discovered through a graph-based evolutionary algorithm GNP, which consequently has the distinct expression ability to model and evolve the "if-then" decision-making rules. Experiments on a benchmark multi-step problem (so-called Reinforcement Learning problem) demonstrate its effectiveness.