Abstract
Previous work [1] introduced a new approach to value function approximation in classifier systems called hyperplane coding. Hyperplane coding is a closely related variation of tile coding [13] in which classifier rule conditions fill the role of tiles, and there are few restrictions on the way those “tiles” are organized. Experiments with hyperplane coding have shown that, given a relatively small population of random classifiers, it computes much better approximations than more conventional classifier system methods in which individual rules compute approximations independently. The obvious next step in this line of research is to use the approximation resources available in a random population as a starting point for a more refined approach to approximation that re-allocates resources adaptively to gain greater precision in those regions of the input space where it is needed. This paper shows how to compute such an adaptive function approximation.
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Booker, L.B. (2007). Adaptive Value Function Approximations in Classifier Systems. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_15
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DOI: https://doi.org/10.1007/978-3-540-71231-2_15
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