PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Selecting the State-Representation in Reinforcement Learning
Odalric-Ambrym Maillard, Daniil Ryabko and Rémi Munos
Advances in Neural Information Processing Systems Number 24, pp. 2627-2635, 2011.

Abstract

The problem of selecting the right state-representation in a reinforcement learning problem is considered. Several models (functions mapping past observations to a finite set) of the observations are given, and it is known that for at least one of these models the resulting state dynamics are indeed Markovian. Without knowing neither which of the models is the correct one, nor what are the probabilistic characteristics of the resulting MDP, it is required to obtain as much reward as the optimal policy for the correct model (or for the best of the correct models, if there are several). We propose an algorithm that achieves that, with a regret of order T^{2/3} where T is the horizon time.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8764
Deposited By:Odalric-Ambrym Maillard
Deposited On:21 February 2012