Neurocomputing: foundations of research
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Kernel-Based Reinforcement Learning
Machine Learning
Classifiers that approximate functions
Natural Computing: an international journal
Off-Policy Temporal Difference Learning with Function Approximation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
An analysis of generalization in the xcs classifier system
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Empirical analysis of generalization and learning in XCS with gradient descent
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Learning classifier system with average reward reinforcement learning
Knowledge-Based Systems
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This paper investigates reinforcement learning (RL) in XCS. First, it formally shows that XCS implements a method of generalized RL based on linear approximators, in which the usual input mapping function translates the state-action space into a niche relative fitness space. Then, it shows that, although XCS has always been related to standard RL, XCS is actually a method of averaging RL. More precisely, XCS with gradient descent can be actually derived from the typical update of averaging RL. It is noted that the use of averaging RL in XCS introduces an intrinsic preference toward classifiers with a smaller fitness in the niche. It is argued that, because of the accuracy pressure in XCS, this results in an additional preference toward specificity. A very simple experiment is presented to support this hypothesis. The same approach is applied to XCS with computed prediction (XCSF) and similar conclusions are drawn.