IDEAS home Printed from https://ideas.repec.org/h/spr/dymchp/978-3-031-85256-5_7.html
   My bibliography  Save this book chapter

Economic Model Predictive Control as a Solution to Markov Decision Processes

Author

Listed:
  • Dirk Reinhardt

    (Norwegian University of Science and Technology (NTNU))

  • Akhil S. Anand

    (Norwegian University of Science and Technology (NTNU))

  • Shambhuraj Sawant

    (Norwegian University of Science and Technology (NTNU))

  • Sébastien Gros

    (Norwegian University of Science and Technology (NTNU))

Abstract

MDPs offer a fairly generic and powerful framework to discuss the notion of optimal policies for dynamic systems, in particular when the dynamics are stochastic. However, computing the optimal policy of an MDP can be very difficult due to the curse of dimensionality present in solving the underlying Bellman equations. MPC is a very popular technique for building control policies for complex dynamic systems. Historically, MPC has focused on constraint satisfaction and steering dynamic systems towards a user-defined reference. More recently, EMPC was proposed as a computationally tractable way of building optimal policies for dynamic systems. When stochasticity is present, EMPC is close to the MDP framework. In that context, EMPC can be construed as a tractable heuristic to provide approximate solutions to MDPs. However, there is arguably a knowledge gap in the literature regarding these approximate solutions and the conditions for an EMPC scheme to achieve closed-loop optimality. This chapter aims to clarify this approximation pedagogically, to provide the conditions for EMPC to deliver optimal policies, and to explore some of their consequences.

Suggested Citation

  • Dirk Reinhardt & Akhil S. Anand & Shambhuraj Sawant & Sébastien Gros, 2025. "Economic Model Predictive Control as a Solution to Markov Decision Processes," Dynamic Modeling and Econometrics in Economics and Finance,, Springer.
    • Handle: RePEc:spr:dymchp:978-3-031-85256-5_7
    DOI: 10.1007/978-3-031-85256-5_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    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:dymchp:978-3-031-85256-5_7. 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.