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Summary: Using Temporal Neighborhoods to Adapt
Function Approximators in Reinforcement
Learning
R. Matthew Kretchmar
Department of Computer Science
Colorado State University
Fort Collins, CO 80523
kretchma@cs.colostate.edu
Charles W. Anderson
Department of Computer Science
Colorado State University
Fort Collins, CO 80523
anderson@cs.colostate.edu
Abstract
To avoid the curse of dimensionality, function approximators are
used in reinforcement learning to learn value functions for individ-
ual states. In order to make better use of computational resources
basis functions many researchers are investigating ways to adapt
the basis functions during the learning process so that they better
t the value-function landscape. Here we introduce temporal neigh-
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