Solution to the risk-sensitive average cost optimality equation in a class of Markov decision processes with finite state space

This work concerns discrete-time Markov decision processes with finite state space and bounded costs per stage. The decision maker ranks random costs via the expectation of the utility function associated to a constant risk sensitivity coefficient, and the performance of a control policy is measured by the corresponding (long-run) risk-sensitive average cost criterion. The main structural restriction on the system is the following communication assumption: For every pair of states x and y, there exists a policy π, possibly depending on x and y, such that when the system evolves under π starting at x, the probability of reaching y is positive. Within this framework, the paper establishes the existence of solutions to the optimality equation whenever the constant risk sensitivity coefficient does not exceed certain positive value.