PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Propagating Uncertainty in POMDP value iteration with Gaussian Processes
Eric P Tuttle and Zoubin Ghahramani
(2004) Technical Report. University College London, London, UK.

Abstract

In this paper, we describe the general approach of trying to solve Partially Observable Markov Decision Processes with approximate value iteration. Methods based on this approach have shown promise for tackling larger problems where exact methods are doomed, but we explain how most of them suffer from the fundamental problem of ignoring information about the uncertainty of their estimates. We then suggest a new method for value iteration which uses Gaussian processes to form a Bayesian representation of the uncertain POMDP value function. We evaluate this method on several standard POMDPs and obtain promising results.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:800
Deposited By:Zoubin Ghahramani
Deposited On:30 December 2004