A simulation‐based approach to stochastic dynamic programming

Nicholas G. Polson and Morten Sorensen

Applied Stochastic Models in Business and Industry, 2011, vol. 27, issue 2, 151-163

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.

Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/asmb.896

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:27:y:2011:i:2:p:151-163

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().