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Locally weighted learning for control




Lazy learning methods provide useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of complex systems. This paper surveys ways in which locally weighted learning, a type of lazy learning, has been applied by us to control tasks. We explain various forms that control tasks can take, and how this affects the choice of learning paradigm. The discussion section explores the interesting impact that explicitly remembering all previous experiences has on the problem of learning to control. Keywords: locally weighted regression, LOESS, LWR, lazy learning, memory-based learning, least commitment learning, forward models, inverse models, linear quadratic regulation (LQR), shifting setpoint algorithm, dynamic programming.

Author(s): Atkeson, C. G. and Moore, A. W. and Schaal, S.
Book Title: Artificial Intelligence Review
Volume: 11
Number (issue): 1-5
Pages: 75-113
Year: 1997

Department(s): Autonomous Motion
Bibtex Type: Article (article)

Cross Ref: p43
Note: clmc
URL: http://www-clmc.usc.edu/publications/A/atkeson-AIR-II-1997.pdf


  title = {Locally weighted learning for control},
  author = {Atkeson, C. G. and Moore, A. W. and Schaal, S.},
  booktitle = {Artificial Intelligence Review},
  volume = {11},
  number = {1-5},
  pages = {75-113},
  year = {1997},
  note = {clmc},
  crossref = {p43},
  url = {http://www-clmc.usc.edu/publications/A/atkeson-AIR-II-1997.pdf}