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Solution Procedures for Partially Observed Markov Decision Processes

Author

Listed:
  • Chelsea C. White

    (University of Virginia, Charlottesville, Virginia)

  • William T. Scherer

    (University of Virginia, Charlottesville, Virginia)

Abstract

We present three algorithms to solve the infinite horizon, expected discounted total reward partially observed Markov decision process (POMDP). Each algorithm integrates a successive approximations algorithm for the POMDP due to A. Smallwood and E. Sondik with an appropriately generalized numerical technique that has been shown to reduce CPU time until convergence for the completely observed case. The first technique is reward revision. The second technique is reward revision integrated with modified policy iteration. The third is a standard extrapolation. A numerical study indicates the potentially significant computational value of these algorithms.

Suggested Citation

  • Chelsea C. White & William T. Scherer, 1989. "Solution Procedures for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 37(5), pages 791-797, October.
    • Handle: RePEc:inm:oropre:v:37:y:1989:i:5:p:791-797
    DOI: 10.1287/opre.37.5.791
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    Citations

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    Cited by:

    1. Abhijit Gosavi, 2009. "Reinforcement Learning: A Tutorial Survey and Recent Advances," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 178-192, May.
    2. Hao Zhang, 2010. "Partially Observable Markov Decision Processes: A Geometric Technique and Analysis," Operations Research, INFORMS, vol. 58(1), pages 214-228, February.
    3. James T. Treharne & Charles R. Sox, 2002. "Adaptive Inventory Control for Nonstationary Demand and Partial Information," Management Science, INFORMS, vol. 48(5), pages 607-624, May.
    4. Serin, Yasemin, 1995. "A nonlinear programming model for partially observable Markov decision processes: Finite horizon case," European Journal of Operational Research, Elsevier, vol. 86(3), pages 549-564, November.
    5. Yanling Chang & Alan Erera & Chelsea White, 2015. "Value of information for a leader–follower partially observed Markov game," Annals of Operations Research, Springer, vol. 235(1), pages 129-153, December.
    6. Abraham Grosfeld‐Nir & Eyal Cohen & Yigal Gerchak, 2007. "Production to order and off‐line inspection when the production process is partially observable," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 845-858, December.
    7. Shoshana Anily & Abraham Grosfeld-Nir, 2006. "An Optimal Lot-Sizing and Offline Inspection Policy in the Case of Nonrigid Demand," Operations Research, INFORMS, vol. 54(2), pages 311-323, April.
    8. Yossi Aviv & Amit Pazgal, 2005. "A Partially Observed Markov Decision Process for Dynamic Pricing," Management Science, INFORMS, vol. 51(9), pages 1400-1416, September.
    9. Chernonog, Tatyana & Avinadav, Tal, 2016. "A two-state partially observable Markov decision process with three actionsAuthor-Name: Ben-Zvi, Tal," European Journal of Operational Research, Elsevier, vol. 254(3), pages 957-967.
    10. Yanling Chang & Alan Erera & Chelsea White, 2015. "A leader–follower partially observed, multiobjective Markov game," Annals of Operations Research, Springer, vol. 235(1), pages 103-128, December.

    More about this item

    Keywords

    dynamic programming: Markov; finite state;

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