Value function approximation on nonlinear manifolds for robot motor control (2007)
| Venue: | In Proceedings of the IEEE Conference on Robots and Automation (ICRA |
| Citations: | 5 - 1 self |
BibTeX
@INPROCEEDINGS{Sugiyama07valuefunction,
author = {Masashi Sugiyama and Hirotaka Hachiya Christopher Towell and Sethu Vijayakumar},
title = {Value function approximation on nonlinear manifolds for robot motor control},
booktitle = {In Proceedings of the IEEE Conference on Robots and Automation (ICRA},
year = {2007}
}
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Abstract
Abstract — The least squares approach works efficiently in value function approximation, given appropriate basis functions. Because of its smoothness, the Gaussian kernel is a popular and useful choice as a basis function. However, it does not allow for discontinuity which typically arises in realworld reinforcement learning tasks. In this paper, we propose a new basis function based on geodesic Gaussian kernels, which exploits the non-linear manifold structure induced by the Markov decision processes. The usefulness of the proposed method is successfully demonstrated in a simulated robot arm control and Khepera robot navigation. I.
