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Gaussian Processes in Reinforcement Learning

2004

Conference Paper

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We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP model allows evaluation of the value function in closed form. The resulting policy iteration algorithm is demonstrated on a simple problem with a two dimensional state space. Further, we speculate that the intrinsic ability of GP models to characterise distributions of functions would allow the method to capture entire distributions over future values instead of merely their expectation, which has traditionally been the focus of much of reinforcement learning.

Author(s): Rasmussen, CE. and Kuss, M.
Book Title: Advances in Neural Information Processing Systems 16
Journal: Advances in Neural Information Processing Systems 16
Pages: 751-759
Year: 2004
Month: June
Day: 0
Editors: Thrun, S., L. K. Saul, B. Sch{\"o}lkopf
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-20152-6
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{2287,
  title = {Gaussian Processes in Reinforcement Learning},
  author = {Rasmussen, CE. and Kuss, M.},
  journal = {Advances in Neural Information Processing Systems 16},
  booktitle = {Advances in Neural Information Processing Systems 16},
  pages = {751-759},
  editors = {Thrun, S., L. K. Saul, B. Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  month_numeric = {6}
}