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An envelope theorem and some applications to discounted Markov decision processes

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  • Hugo Cruz-Suárez
  • Raúl Montes-de-Oca

Abstract

In this paper, an Envelope Theorem (ET) will be established for optimization problems on Euclidean spaces. In general, the Envelope Theorems permit analyzing an optimization problem and giving the solution by means of differentiability techniques. The ET will be presented in two versions. One of them uses concavity assumptions, whereas the other one does not require such kind of assumptions. Thereafter, the ET established will be applied to the Markov Decision Processes (MDPs) on Euclidean spaces, discounted and with infinite horizon. As the first application, several examples (including some economic models) of discounted MDPs for which the et allows to determine the value iteration functions will be presented. This will permit to obtain the corresponding optimal value functions and the optimal policies. As the second application of the ET, it will be proved that under differentiability conditions in the transition law, in the reward function, and the noise of the system, the value function and the optimal policy of the problem are differentiable with respect to the state of the system. Besides, various examples to illustrate these differentiability conditions will be provided. Copyright Springer-Verlag 2008

Suggested Citation

  • Hugo Cruz-Suárez & Raúl Montes-de-Oca, 2008. "An envelope theorem and some applications to discounted Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 67(2), pages 299-321, April.
    • Handle: RePEc:spr:mathme:v:67:y:2008:i:2:p:299-321
    DOI: 10.1007/s00186-007-0155-z
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    References listed on IDEAS

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    1. Gladys Denisse Salgado Su¨¢rez & Hugo Cruz-Su¨¢rez & Jos¨¦ Dionicio Zacar¨ªas Flores, 2018. "Asymptotic Analysis of a Deterministic Control System via Euler's Equation Approach," Journal of Mathematics Research, Canadian Center of Science and Education, vol. 10(1), pages 115-123, February.
    2. Mauro Gaggero & Giorgio Gnecco & Marcello Sanguineti, 2014. "Approximate dynamic programming for stochastic N-stage optimization with application to optimal consumption under uncertainty," Computational Optimization and Applications, Springer, vol. 58(1), pages 31-85, May.

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