Controlled Markov chains with constraints

We consider the ergodic control of a Markov chain on a countable state space with a compact action space in presence of finitely many (say, m) ergodic constraints. Under a condition on the cost functions that penalizes instability, the existence of an optimal stable stationary strategy randomized at a maximum of m states is established using convex analytic arguments