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Finite approximation for finite-horizon continuous-time Markov decision processes

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  • Qingda Wei

    (Huaqiao University)

Abstract

In this paper we study the continuous-time Markov decision processes with a denumerable state space, a Borel action space, and unbounded transition and cost rates. The optimality criterion to be considered is the finite-horizon expected total cost criterion. Under the suitable conditions, we propose a finite approximation for the approximate computations of an optimal policy and the value function, and obtain the corresponding error estimations. Furthermore, our main results are illustrated with a controlled birth and death system.

Suggested Citation

  • Qingda Wei, 2017. "Finite approximation for finite-horizon continuous-time Markov decision processes," 4OR, Springer, vol. 15(1), pages 67-84, March.
    • Handle: RePEc:spr:aqjoor:v:15:y:2017:i:1:d:10.1007_s10288-016-0321-3
    DOI: 10.1007/s10288-016-0321-3
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    1. van Dijk, Nico M., 1988. "On the finite horizon Bellman equation for controlled Markov jump models with unbounded characteristics: existence and approximation," Stochastic Processes and their Applications, Elsevier, vol. 28(1), pages 141-157, April.
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    3. Guo, Xianping & Zhang, Wenzhao, 2014. "Convergence of controlled models and finite-state approximation for discounted continuous-time Markov decision processes with constraints," European Journal of Operational Research, Elsevier, vol. 238(2), pages 486-496.
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