Using Randomization to Break the Curse of Dimensionality

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Published in RePEc

Abstract

This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions to a class of finite and infinite horizon Markovian decision problems (MDPs). We prove that these algorithms succeed in breaking the curse of dimensionality for a subclass of MDPs known as discrete decision processes (DDPs).

Keywords

SUCCESSIVE APPROXIMATIONS
CURSE OF DIMENSIONALITY
USING RANDOMIZATION
BREAKING THE CURSE
ALGORITHMS
MULTIGRID
MDPS

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