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A simulation-based approach to stochastic dynamic programming

✍ Scribed by Nicholas G. Polson; Morten Sorensen

Publisher: John Wiley and Sons
Year: 2011
Tongue: English
Weight: 492 KB
Volume: 27
Category: Article
ISSN: 1524-1904
DOI: 10.1002/asmb.896

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✦ Synopsis

Abstract

In this paper we develop a simulation‐based approach to stochastic dynamic programming. To solve the Bellman equation we construct Monte Carlo estimates of Q‐values. Our method is scalable to high dimensions and works in both continuous and discrete state and decision spaces while avoiding discretization errors that plague traditional methods. We provide a geometric convergence rate. We illustrate our methodology with a dynamic stochastic investment problem. Copyright © 2011 John Wiley & Sons, Ltd.

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