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Value-Function Approximations for Partially Observable Markov Decision Processes

. Journal of Artificial Intelligence Research (2000)

Abstract

Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price | exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on ecient...

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