This note concerns discrete-time Markov decision processes with denumerable state space. A control policy is graded by the long-run expected average reward criterion, and the main feature of the model is that the reward function and the transition law depend on an unknown parameter. Besides...

This note concerns discrete-time Markov decision processes with denumerable state space. A control policy is graded by the long-run expected average reward criterion, and the main feature of the model is that the reward function and the transition law depend on an unknown parameter. Besides...

This note concerns Markov decision processes on a discrete state space. It is supposed that the reward function is nonnegative, and that the decision maker has a nonnull constant risk-sensitivity, which leads to grade random rewards via the expectation of an exponential utility function. The...

This note concerns Markov decision processes on a discrete state space. It is supposed that the reward function is nonnegative, and that the decision maker has a nonnull constant risk-sensitivity, which leads to grade random rewards via the expectation of an exponential utility function. The...

We study controlled Markov chains with denumerable state space and bounded costs per stage. A (long-run) risk-sensitive average cost criterion, associated to an exponential utility function with a constant risk sensitivity coefficient, is used as a performance measure. The main assumption on the...

This work concerns controlled Markov chains with denumerable state space and discrete time parameter. The reward function is assumed to be≤0 and the performance of a control policy is measured by the expected total-reward criterion. Within this context, sufficient conditions are given so that...