Feature Discovery in Approximate Dynamic Programming

Philippe Preux 1, 2 Sertan Girgin 1, 2 Manuel Loth 1, 2
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : Feature discovery aims at finding the best representation of data. This is a very important topic in machine learning, and in reinforcement learning in particular. Based on our recent work on feature discovery in the context of reinforcement learning to discover a good, if not the best, representation of states, we report here on the use of the same kind of approach in the context of approximate dynamic programming. The striking difference with the usual approach is that we use a non parametric function approximator to represent the value function, instead of a parametric one. We also argue that the problem of discovering the best state representation and the problem of the value function approximation are just the two faces of the same coin, and that using a non parametric approach provides an elegant solution to both problems at once.
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Contributor : Preux Philippe <>
Submitted on : Thursday, January 8, 2009 - 3:03:43 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM


  • HAL Id : hal-00351144, version 1



Philippe Preux, Sertan Girgin, Manuel Loth. Feature Discovery in Approximate Dynamic Programming. Approximate Dynamic Programming and Reinforcement Learning, Mar 2009, Nashville, United States. ⟨hal-00351144⟩



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