1,721,286 research outputs found
A Comparison of Relative Accuracy and Raw Accuracy in XCS
No full textIn XCS classifier fitness is measured as the relative accuracy of classifier prediction. A classifier is fit if its prediction of the expected payoff is more accurate than that provided by the other classifiers that appear in the same environmental niches. We introduce a modification of Wilson's original definition in which classifier fitness is measured as the absolute (raw) accuracy of classifier prediction. A classifier is fit if the error affecting its prediction is smaller than a given threshold. Then we compare Wilson's relative accuracy and raw accuracy on a number of problems both in terms of learning performance and in terms of generalization capabilities
Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding
No full text
Learning classifier systems from a reinforcement learning perspective.
No full textReinforcement learning is defined as the problem of an agent that learns to perform a certain task through trial and error interactions with an unknown environment [27]. Most of the research in reinforcement learning focuses on algorithms that are inspired, in a way or another, by methods of Dynamic Programming (e.g., Watkins’ Q-learning [29]). These algorithms have a strong theoretical framework but assume a tabular representation of the value function; thus, their applicability is limited to problems involving few input states and few actions. Alternatively, these methods can be extended for large applications by using function approximators (e.g., neural networks) to represent the value function [27]. In these cases, the general theoretical framework remains but convergence theorems no longer apply
- …
