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| Record Number | 43 |
| Reference Type | Journal Article |
| Author(s) | Atkeson, C. G.;Moore, A. W.;Schaal, S. |
| Year | 1997 |
| Title | Locally weighted learning for control |
| Journal/Conference/Book Title | Artificial Intelligence Review |
| Label | Atke97c |
| Keywords | statistical learning, nonparametric regression, distance metric, lazy learning, learning control, reinforcement learning |
Abstract |
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.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/A/atkeson-AIR-II-1997.pdf
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| Volume | 11 |
| Number | 1-5 |
| Pages | 75-113 |
| Short Title | Locally weighted learning for control |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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Page last modified on August 10, 2006, at 06:47 PM
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