Non-randomized control of constrained Markov decision processes

This paper presents results concerning the optimal control of constrained Markov decision processes with expected-cost criteria using non-randomized policies. A dynamic programming approach is used to construct optimal policies. The convergence of the finite horizon value function to the infinite horizon value function is also shown. A simple example application is presented