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| Record Number | 2655 |
| Reference Type | Conference Proceedings |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2006 |
| Title | Policy gradient methods for robotics |
| Journal/Conference/Book Title | Proceedings of the IEEE International Conference on Intelligent Robotics Systems (IROS 2006) |
| Keywords | policy gradient methods, reinforcement learning, robotics |
Abstract |
The aquisition and improvement of motor skills and
control policies for robotics from trial and error is of essential
importance if robots should ever leave precisely pre-structured
environments. However, to date only few existing reinforcement
learning methods have been scaled into the domains of highdimensional
robots such as manipulator, legged or humanoid
robots. Policy gradient methods remain one of the few exceptions
and have found a variety of applications. Nevertheless, the
application of such methods is not without peril if done in an uninformed
manner. In this paper, we give an overview on learning
with policy gradient methods for robotics with a strong focus on
recent advances in the field. We outline previous applications to
robotics and show how the most recently developed methods can
significantly improve learning performance. Finally, we evaluate
our most promising algorithm in the application of hitting a
baseball with an anthropomorphic arm.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IROS2006.pdf
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| Place Published | Beijing, Oct. 9-15 |
| Short Title | Policy gradient methods for robotics |
| 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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