Locally Weighted Learning for Control

Authors:
Christopher G. Atkeson;Andrew W. Moore;Stefan Schaal
Affiliations:
College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA 30332-0280. E-mail: cga@cc.gatech.edu, sschaal@cc.gatech.edu;Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213. E-mail: awm@cs.cmu.edu;College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA 30332-0280. E-mail: cga@cc.gatech.edu, sschaal@cc.gatech.edu
Venue:
Artificial Intelligence Review - Special issue on lazy learning
Year:
1997

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Abstract

Lazy learning methods provide useful representations and training algorithmsfor learning about complex phenomena during autonomous adaptive control ofcomplex systems. This paper surveys ways in which locally weighted learning,a type of lazy learning, has been applied by us to control tasks. We explainvarious forms that control tasks can take, and how this affects the choiceof learning paradigm. The discussion section explores the interesting impactthat explicitly remembering all previous experiences has on the problem oflearning to control.