- 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.
- Copyright:
- 2007 by The University of Edinburgh. All Rights Reserved
- Links To Paper
- No links available
- Bibtex format
- @InProceedings{EDI-INF-RR-0948,
- author = {
Masashi Sugiyama
and Hirotaka Hachiya
and Christopher Towell
and Sethu Vijayakumar
},
- title = {Value Function Approximation on Non-Linear Manifolds for Robot Motor Control},
- book title = {Robotics and Automation, 2007 IEEE International Conference on},
- publisher = {IEEE},
- year = 2007,
- month = {Apr},
- pages = {1733-1740},
- doi = {10.1109/ROBOT.2007.363573},
- }
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