Gaussian Processes in Reinforcement Learning

Rasmussen, CE; Kuss, M

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Gaussian Processes in Reinforcement Learning

Rasmussen, C., & Kuss, M. (2004). Gaussian Processes in Reinforcement Learning. In S. Thrun, L. Saul, & B. Schölkopf (Eds.), Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference (pp. 751-759). Cambridge, MA, USA: MIT Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D8E5-F Version Permalink: https://hdl.handle.net/21.11116/0000-0005-4650-2

Genre: Conference Paper

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https://papers.nips.cc/paper/2420-gaussian-processes-in-reinforcement-learning.pdf (Publisher version) Open Access status unknown

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Creators:
Rasmussen, CE^{1, 2}, Author
Kuss, M^{1, 2}, Author

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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Abstract: 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.

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Dates: Date issued: 2004-06

Publication Status: Issued

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Identifiers: BibTex Citekey: 2287

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Title: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)

Place of Event: Vancouver, BC, Canada

Start-/End Date: 2003-12-08 - 2003-12-13

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Title: Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference

Source Genre: Proceedings

Creator(s):
Thrun, S¹, Editor
Saul, LK, Editor
Schölkopf, B¹, Editor

Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794

Publ. Info: Cambridge, MA, USA : MIT Press

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 751 - 759 Identifier: ISBN: 0-262-20152-6