Abstract: |
Hyperplane coding is a new, closely related variation of tile coding in which classifier rule conditions fill the role of tiles, and there are few restrictions on the way those ``tiles'' are organised. The basic idea is to treat rules as features that collectively specify a linear gradient-descent function approximator. The hypothesis behind this idea is that classifier rules can be more effective as function approximators if they work together to implement a distributed, coarse-coded representation of the value function. One open question remaining about hyperplane coding is how the quality of the approximation is affected by the set of classifiers in the population. A random population of classifiers is sufficient to obtain high quality results. Would a more carefully chosen population do even better? The obvious next step in this research is to use the approximation resources available in a random population as a starting point for a more refined approach to approximation that reallocates 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. |
