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| Record Number | 2675 |
| Reference Type | Conference Paper |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2007 |
| Title | Reinforcement learning by reward-weighted regression for operational space control |
| Journal/Conference/Book Title | Proceedings of the International Conference on Machine Learning (ICML2007) |
| Keywords | reinforcement learning, operational space control, weighted regression |
Abstract |
Many robot control problems of practical importance, including
operational space control, can be reformulated as immediate reward
reinforcement learning problems. However, few of the known
optimization or reinforcement learning algorithms can be used in
online learning control for robots, as they are either prohibitively
slow, do not scale to interesting domains of complex robots, or
require trying out policies generated by random search, which are
infeasible for a physical system. Using a generalization of the
EM-base reinforcement learning framework suggested by Dayan &
Hinton, we reduce the problem of learning with immediate rewards to a
reward-weighted regression problem with an adaptive, integrated reward
transformation for faster convergence. The resulting algorithm is
efficient, learns smoothly without dangerous jumps in solution space,
and works well in applications of complex high degree-of-freedom robots.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications//P/peters_ICML2007.pdf
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| Place Published | Corvallis, Oregon, June 19-21 |
| Short Title | Reinforcement learning by reward-weighted regression for operational space 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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