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Decomposition Methods for Solving Finite-Horizon Large MDPs

Author

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  • Bouchra el Akraoui
  • Cherki Daoui
  • Abdelhadi Larach
  • khalid Rahhali
  • Efthymios G. Tsionas

Abstract

Conventional algorithms for solving Markov decision processes (MDPs) become intractable for a large finite state and action spaces. Several studies have been devoted to this issue, but most of them only treat infinite-horizon MDPs. This paper is one of the first works to deal with non-stationary finite-horizon MDPs by proposing a new decomposition approach, which consists in partitioning the problem into smaller restricted finite-horizon MDPs, each restricted MDP is solved independently, in a specific order, using the proposed hierarchical backward induction (HBI) algorithm based on the backward induction (BI) algorithm. Next, the sub-local solutions are combined to obtain a global solution. An example of racetrack problems shows the performance of the proposal decomposition technique.

Suggested Citation

  • Bouchra el Akraoui & Cherki Daoui & Abdelhadi Larach & khalid Rahhali & Efthymios G. Tsionas, 2022. "Decomposition Methods for Solving Finite-Horizon Large MDPs," Journal of Mathematics, Hindawi, vol. 2022, pages 1-8, August.
    • Handle: RePEc:hin:jjmath:8404716
    DOI: 10.1155/2022/8404716
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    Cited by:

    1. Bouchra El Akraoui & Daoui Cherki, 2023. "Solving Finite-Horizon Discounted Non-Stationary MDPS," Folia Oeconomica Stetinensia, Sciendo, vol. 23(1), pages 1-15, June.

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