PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature Discovery in Approximate Dynamic Programming
Philippe Preux, Sertan Girgin and Manuel Loth
In: Approximate Dynamic Programming and Reinforcement Learning, 30 Mar-2 Apr, Nashville, USA.

Abstract

Feature discovery aims at finding the best repre- sentation 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, repre- sentation 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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5968
Deposited By:Philippe Preux
Deposited On:08 March 2010