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A simulation‐based approach to stochastic dynamic programming

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  • Nicholas G. Polson
  • Morten Sorensen

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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  • Nicholas G. Polson & Morten Sorensen, 2011. "A simulation‐based approach to stochastic dynamic programming," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 27(2), pages 151-163, March.
    • Handle: RePEc:wly:apsmbi:v:27:y:2011:i:2:p:151-163
    DOI: 10.1002/asmb.896
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    Cited by:

    1. Insua, David Rios & Ruggeri, Fabrizio & Soyer, Refik & Wilson, Simon, 2020. "Advances in Bayesian decision making in reliability," European Journal of Operational Research, Elsevier, vol. 282(1), pages 1-18.

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