Advances in Operations Research
Volume 2010 (2010), Article ID 659432, 19 pages
Review Article

Exact Decomposition Approaches for Markov Decision Processes: A Survey

1Département de Mathématiques, Faculté des Sciences et Techniques, P.O. Box 523, Béni-Mellal 23000, Morocco
2Département de Mathématiques, Faculté des Sciences, P.O. Box 1014, Rabat 10000, Morocco
3Département Génie Industriel, Ecole Mohammedia d'Ingénieurs,P.O. Box 765, Rabat 10000, Morocco

Received 27 August 2009; Revised 26 March 2010; Accepted 24 May 2010

Academic Editor: Imed Kacem

Copyright © 2010 Cherki Daoui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As classical methods are intractable for solving Markov decision processes (MDPs) requiring a large state space, decomposition and aggregation techniques are very useful to cope with large problems. These techniques are in general a special case of the classic Divide-and-Conquer framework to split a large, unwieldy problem into smaller components and solving the parts in order to construct the global solution. This paper reviews most of decomposition approaches encountered in the associated literature over the past two decades, weighing their pros and cons. We consider several categories of MDPs (average, discounted, and weighted MDPs), and we present briefly a variety of methodologies to find or approximate optimal strategies.