Adaptive Value Function Approximations in Classifier Systems
June 2005
Lashon B. Booker, The MITRE Corporation
ABSTRACT
Considerable attention has been paid to the issue of value
function approximation in the reinforcement learning literature
[3]. One of the fundamental assumptions underlying algorithms
for solving reinforcement learning problems is that
states and state-action pairs have well-defined values that
can be approximated and used to help determine an optimal
policy. The quality of those approximations is a critical
factor in determining the success of many algorithms in solving
reinforcement learning problems.
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