Optimality criteria for Markov decision processes have historically been based on a risk neutral formulation of the decision maker's preferences. An explicit utility formulation, incorporating both ...

The two most commonly considered reward criteria for Markov decision processes are the discounted reward and the long-term average reward. The first tends to "neglect" the future, concentrating on the ...

Markov decision processes (MDPs) and stochastic control constitute pivotal frameworks for modelling decision-making in systems subject to uncertainty.