In this note we investigate a time-discrete stochastic dynamic programming problem with countable state and action spaces. We introduce an approximation procedure for a numerical solution by decomposition of the state and also of the action space. The minimal value functions and the optimal policies of the Markovian Decision .Processes constructed by clustering of both spaces are calculated by dynamic programming. Bounds for the minimal value functions will be obtained and convergence theorems are proved.