Comparison of CMACs and radial basis functions for local function approximators in reinforcement learning

CMACs and radial basis functions are often used in reinforcement learning to learn value function approximations having local generalization properties. We examine the similarities and differences between CMACs, RBFs and normalized RBFs and compare the performance of Q-learning with each representat...

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Bibliographic Details
Published in1997 IEEE International Conference on Neural Networks Vol. 2; pp. 834 - 837 vol.2
Main Authors Kretchmar, R.M., Anderson, C.W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Adaptive control
Computer science
Function approximation
Learning
Programmable control
Shape
State-space methods
Table lookup
Tiles
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ISBN0780341228
9780780341227
DOI10.1109/ICNN.1997.616132

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Summary:CMACs and radial basis functions are often used in reinforcement learning to learn value function approximations having local generalization properties. We examine the similarities and differences between CMACs, RBFs and normalized RBFs and compare the performance of Q-learning with each representation applied to the mountain car problem. We discuss ongoing research efforts to exploit the flexibility of adaptive units to better represent the local characteristics of the state space.
ISBN:0780341228
9780780341227
DOI:10.1109/ICNN.1997.616132