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.