Markov decision processes which allow for an unbounded reward structure are considered. Conditions are given which allow successive approximations with a convergence in some strong sense. This "strong" convergence enables the construction of upper and lower bounds.
The conditions are weaker than those proposed by Lippman , Harrison  and Wessels  and are in fact a slight generalization of the conditions proposed by Van Nunen .
A successive approximation algorithm will be indicated. The conditions will be analysed and compared with those in literature.
|ISSN van geprinte versie||0926-4493|