Decomposition Methods for Solving Finite-Horizon Large MDPs

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 fin...

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Main Authors: Bouchra el Akraoui, Cherki Daoui, Abdelhadi Larach, khalid Rahhali
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Mathematics
Online Access:http://dx.doi.org/10.1155/2022/8404716
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author Bouchra el Akraoui
Cherki Daoui
Abdelhadi Larach
khalid Rahhali
author_facet Bouchra el Akraoui
Cherki Daoui
Abdelhadi Larach
khalid Rahhali
author_sort Bouchra el Akraoui
collection DOAJ
description 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.
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id doaj-art-5092dd93d61d40f0888530495d994132
institution Kabale University
issn 2314-4785
language English
publishDate 2022-01-01
publisher Wiley
record_format Article
series Journal of Mathematics
spelling doaj-art-5092dd93d61d40f0888530495d9941322025-02-03T06:04:53ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/8404716Decomposition Methods for Solving Finite-Horizon Large MDPsBouchra el Akraoui0Cherki Daoui1Abdelhadi Larach2khalid Rahhali3Laboratory of Information Processing and Decision SupportLaboratory of Information Processing and Decision SupportLaboratory of Information Processing and Decision SupportLoboratory of MathematicsConventional 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.http://dx.doi.org/10.1155/2022/8404716
spellingShingle Bouchra el Akraoui
Cherki Daoui
Abdelhadi Larach
khalid Rahhali
Decomposition Methods for Solving Finite-Horizon Large MDPs
Journal of Mathematics
title Decomposition Methods for Solving Finite-Horizon Large MDPs
title_full Decomposition Methods for Solving Finite-Horizon Large MDPs
title_fullStr Decomposition Methods for Solving Finite-Horizon Large MDPs
title_full_unstemmed Decomposition Methods for Solving Finite-Horizon Large MDPs
title_short Decomposition Methods for Solving Finite-Horizon Large MDPs
title_sort decomposition methods for solving finite horizon large mdps
url http://dx.doi.org/10.1155/2022/8404716
work_keys_str_mv AT bouchraelakraoui decompositionmethodsforsolvingfinitehorizonlargemdps
AT cherkidaoui decompositionmethodsforsolvingfinitehorizonlargemdps
AT abdelhadilarach decompositionmethodsforsolvingfinitehorizonlargemdps
AT khalidrahhali decompositionmethodsforsolvingfinitehorizonlargemdps