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  A Non-Parametric Approach to Dynamic Programming

Kroemer, O., & Peters, J. (2012). A Non-Parametric Approach to Dynamic Programming. In J. Shawe-Taylor (Ed.), Advances in Neural Information Processing Systems 24 (pp. 1719-1727). Red Hook, NY, USA: Curran.

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 Creators:
Kroemer, O1, Author              
Peters, J1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: In this paper, we consider the problem of policy evaluation for continuousstate systems. We present a non-parametric approach to policy evaluation, which uses kernel density estimation to represent the system. The true form of the value function for this model can be determined, and can be computed using Galerkin’s method. Furthermore, we also present a unified view of several well-known policy evaluation methods. In particular, we show that the same Galerkin method can be used to derive Least-Squares Temporal Difference learning, Kernelized Temporal Difference learning, and a discrete-state Dynamic Programming solution, as well as our proposed method. In a numerical evaluation of these algorithms, the proposed approach performed better than the other methods.

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 Dates: 2012-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: KroemerP2011
 Degree: -

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Title: Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
Place of Event: Granada, Spain
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Title: Advances in Neural Information Processing Systems 24
Source Genre: Proceedings
 Creator(s):
Shawe-Taylor, J, Editor
Zemel, RS, Author
Bartlett, P, Author
Pereira, F, Author
Weinberger, KQ, Author
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1719 - 1727 Identifier: ISBN: 978-1-618-39599-3