IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v30y1982i1p62-73.html
   My bibliography  Save this article

An Iterative Aggregation Procedure for Markov Decision Processes

Author

Listed:
  • Roy Mendelssohn

    (National Marine Fisheries Service, NOAA, Honolulu, Hawaii)

Abstract

An iterative aggregation procedure is described for solving large scale, finite state, finite action Markov decision processes (MDPs). At each iteration, an aggregate master problem and a sequence of smaller subproblems are solved. The weights used to form the aggregate master problem are based on the estimates from the previous iteration. Each subproblem is a finite state, finite action MDP with a reduced state space and unequal row sums. Global convergence of the algorithm is proven under very weak assumptions. The proof relates this technique to other iterative methods that have been suggested for general linear programs.

Suggested Citation

  • Roy Mendelssohn, 1982. "An Iterative Aggregation Procedure for Markov Decision Processes," Operations Research, INFORMS, vol. 30(1), pages 62-73, February.
    • Handle: RePEc:inm:oropre:v:30:y:1982:i:1:p:62-73
    DOI: 10.1287/opre.30.1.62
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.30.1.62
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.30.1.62?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. Jie Ning & Matthew J. Sobel, 2019. "Easy Affine Markov Decision Processes," Operations Research, INFORMS, vol. 67(6), pages 1719-1737, November.
    2. Chevalier, Philippe & Lamas, Alejandro & Lu, Liang & Mlinar, Tanja, 2015. "Revenue management for operations with urgent orders," European Journal of Operational Research, Elsevier, vol. 240(2), pages 476-487.
    3. Ali Fattahi & Sriram Dasu & Reza Ahmadi, 2023. "Peak-Load Energy Management by Direct Load Control Contracts," Management Science, INFORMS, vol. 69(5), pages 2788-2813, May.
    4. Michael Z. Spivey & Warren B. Powell, 2004. "The Dynamic Assignment Problem," Transportation Science, INFORMS, vol. 38(4), pages 399-419, November.
    5. Benjamin Van Roy, 2006. "Performance Loss Bounds for Approximate Value Iteration with State Aggregation," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 234-244, May.

    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:30:y:1982:i:1:p:62-73. 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.