IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v42y1994i3p439-455.html
   My bibliography  Save this article

Finite-Memory Suboptimal Design for Partially Observed Markov Decision Processes

Author

Listed:
  • Chelsea C. White

    (University of Michigan, Ann Arbor, Michigan)

  • William T. Scherer

    (University of Virginia, Charlottesville, Virginia)

Abstract

We develop bounds on the value function and a suboptimal design for the partially observed Markov decision process. These bounds and suboptimal design are based on the M most recent observations and actions. An a priori measure of the quality of these bounds is given. We show that larger M implies tighter bounds. An operations count analysis indicates that ( # A # Z ) M +1 ( # S ) multiplications and additions are required per successive approximations iteration of the suboptimal design algorithm, where A , Z , and S are the action, observation, and state spaces, respectively, suggesting the algorithm is of potential use for problems with large state spaces. A preliminary numerical study indicates that the quality of the suboptimal design can be excellent.

Suggested Citation

  • Chelsea C. White & William T. Scherer, 1994. "Finite-Memory Suboptimal Design for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 42(3), pages 439-455, June.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:3:p:439-455
    DOI: 10.1287/opre.42.3.439
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.42.3.439
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.42.3.439?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Hao Zhang, 2010. "Partially Observable Markov Decision Processes: A Geometric Technique and Analysis," Operations Research, INFORMS, vol. 58(1), pages 214-228, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:42:y:1994:i:3:p:439-455. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.