IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v6y2025i4d10.1007_s43069-025-00468-3.html
   My bibliography  Save this article

Solution for Infinite Horizon Double-Factored Markov Decision Processes with Application

Author

Listed:
  • Chengjun Hou

    (Software Research and Data Science, Amazon Robotics)

Abstract

The double-factored Markov decision process (DFMDP) is a new framework for addressing the Markov decision processes with uncertainty of parameter scenarios. The two factors in this framework refer to the physical state and the scenario belief, which describes the probability distribution of scenarios, and they compose the state-belief pair for the framework. This study focuses on infinite horizon DFMDPs and their application. The optimality equations for infinite horizon DFMDPs are formulated and they can be represented by a value function mapping, which is a contraction under the supremum norm. It is demonstrated that for a fixed state, the optimal value functions for finite horizon DFMDPs are piecewise linear and convex in a scenario belief space. This property is used to develop an algorithm named as the double-factored linear support for an approximate solution to infinite horizon DFMDPs. The principle and framework of the algorithm are described in detail. Three computational instances are presented to illustrate the performance of the algorithm and the applications of infinite horizon DFMDPs.

Suggested Citation

  • Chengjun Hou, 2025. "Solution for Infinite Horizon Double-Factored Markov Decision Processes with Application," SN Operations Research Forum, Springer, vol. 6(4), pages 1-25, December.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:4:d:10.1007_s43069-025-00468-3
    DOI: 10.1007/s43069-025-00468-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-025-00468-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-025-00468-3?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:spr:snopef:v:6:y:2025:i:4:d:10.1007_s43069-025-00468-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.