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Markov Decision Processes with Sample Path Constraints: The Communicating Case

Author

Listed:
  • Keith W. Ross

    (University of Pennsylvania, Philadelphia, Pennsylvania)

  • Ravi Varadarajan

    (University of Florida, Gainesville, Florida)

Abstract

We consider time-average Markov Decision Processes (MDPs), which accumulate a reward and cost at each decision epoch. A policy meets the sample-path constraint if the time-average cost is below a specified value with probability one. The optimization problem is to maximize the expected average reward over all policies that meet the sample-path constraint. The sample-path constraint is compared with the more commonly studied constraint of requiring the average expected cost to be less than a specified value. Although the two criteria are equivalent for certain classes of MDPs, their feasible and optimal policies differ for many nontrivial problems. In general, there does not exist optimal or nearly optimal stationary policies when the expected average-cost constraint is employed. Assuming that a policy exists that meets the sample-path constraint, we establish that there exist nearly optimal stationary policies for communicating MDPs. A parametric linear programming algorithm is given to construct nearly optimal stationary policies. The discussion relies on well known results from the theory of stochastic processes and linear programming. The techniques lead to simple proofs of the existence of optimal and nearly optimal stationary policies for unichain and deterministic MDPs, respectively.

Suggested Citation

  • Keith W. Ross & Ravi Varadarajan, 1989. "Markov Decision Processes with Sample Path Constraints: The Communicating Case," Operations Research, INFORMS, vol. 37(5), pages 780-790, October.
    • Handle: RePEc:inm:oropre:v:37:y:1989:i:5:p:780-790
    DOI: 10.1287/opre.37.5.780
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    Citations

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    Cited by:

    1. Yasemin Serin & Zeynep Muge Avsar, 1997. "Markov decision processes with restricted observations: Finite horizon case," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(5), pages 439-456, August.
    2. Golan, Michal & Shimkin, Nahum, 2024. "Markov decision processes with burstiness constraints," European Journal of Operational Research, Elsevier, vol. 312(3), pages 877-889.
    3. Ohlmann, Jeffrey W. & Bean, James C., 2009. "Resource-constrained management of heterogeneous assets with stochastic deterioration," European Journal of Operational Research, Elsevier, vol. 199(1), pages 198-208, November.
    4. Dmitry Krass & O. J. Vrieze, 2002. "Achieving Target State-Action Frequencies in Multichain Average-Reward Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 27(3), pages 545-566, August.
    5. Q. Zhu, 2007. "Sample-path optimality and variance-maximization for Markov decision processes," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 65(3), pages 519-538, June.

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