New sufficient conditions for average optimality in continuous-time Markov decision processes

This paper is devoted to studying continuous-time Markov decision processes with general state and action spaces, under the long-run expected average reward criterion. The transition rates of the underlying continuous-time Markov processes are allowed to be unbounded, and the reward rates may have neither upper nor lower bounds. We provide new sufficient conditions for the existence of average optimal policies. Moreover, such sufficient conditions are imposed on the controlled process' primitive data and thus they are directly verifiable. Finally, we apply our results to two new examples.