Markov decision processes (MDPs) and stochastic control constitute pivotal frameworks for modelling decision-making in systems subject to uncertainty. At their core, MDPs provide a structured means to ...

Probabilistic model checking and Markov decision processes (MDPs) form two interlinked branches of formal analysis for systems operating under uncertainty. These techniques offer a mathematical ...

We consider discounted Markov decision processes (MDPs) with countably-infinite state spaces, finite action spaces, and unbounded rewards. Typical examples of such MDPs are inventory management and ...

We consider Markov decision processes with unknown transition probabilities and unknown single-period expected cost functions, and we study a method for estimating these quantities from historical or ...

Condition-based maintenance (CBM) focuses on scheduling interventions according to the real-time health state of a system, ...