Mathematical Methods of Operations Research

, Volume 66, Issue 1, pp 165–179 | Cite as

Non-randomized policies for constrained Markov decision processes

Original Article

Abstract

This paper addresses constrained Markov decision processes, with expected discounted total cost criteria, which are controlled by non-randomized policies. A dynamic programming approach is used to construct optimal policies. The convergence of the series of finite horizon value functions to the infinite horizon value function is also shown. A simple example illustrating an application is presented.

Keywords

Constrained Markov Decision processes Dynamic programming Non-randomized policies 

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References

  1. Altman E, Shwartz A (1991) Sensitivity of constrained Markov decision processes. Ann Oper Res 32:1–22MATHCrossRefGoogle Scholar
  2. Chen RC (2004) Constrained stochastic control and optimal search. In: Proceedings of the 43rd IEEE conference on decision and control, Bahamas, vol 3, pp 3013-3020, 14–173Google Scholar
  3. Chen RC (2005) Constrained Markov processes and optimal truncated sequential detection. In: Defence applications of signal processing proceedings, Utah, pp 28–31Google Scholar
  4. Chen RC, Blankenship GL (2002) Dynamic programming equations for constrained stochastic control. In: Proceedings of the 2002 American control conference, Anchorage, AK, vol 3, pp 2014–2022, 8–10Google Scholar
  5. Chen RC, Blankenship GL (2004) Dynamic programming equations for discounted constrained stochastic control. IEEE Trans. Autom. Control 49(5):699–709CrossRefGoogle Scholar
  6. Coraluppi S, Marcus SI (2000) Mixed risk-neutral/minimax control of discrete-time, finite-state Markov decision processes. IEEE Trans Autom Control 45(3):528–532MATHCrossRefGoogle Scholar
  7. Piunovskiy AB, Mao X (2000) Constrained Markovian decision processes: the dynamic programming approach. Oper Res Lett 27:119–126MATHCrossRefGoogle Scholar
  8. Rockafellar RT, Wets RJ-B (1998) Variational analysis. Springer, Berlin Heidelberg New YorkMATHGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Radar DivisionNaval Research LaboratoryWashingtonUSA
  2. 2.Department of Applied Mathematics and StatisticsState University of New YorkStony BrookUSA

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