Robot Learning

Suraiya Jabin
Sciyo, 2010 - 150 Seiten
In this chapter, I have presented Learning Classifier Systems, which add to the classical Reinforcement Learning framework the possibility of representing the state as a vector of attributes and finding a compact expression of the representation so induced. Their formalism conveys a nice interaction between learning and evolution, which makes them a class of particularly rich systems, at the intersection of several research domains. As a result, they profit from the accumulated extensions of these domains. I hope that this presentation has given to the interested reader an appropriate starting point to investigate the different streams of research that underlie the rapid evolution of LCS. In particular, a key starting point is the website dedicated to the LCS community, which can be found at the following URL:

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