On reachability of Markov decision processes: a novel state-classification-based PI approach

This paper concentrates on the reliability of a discrete-time controlled Markov system with finite states and actions, and aims to give an efficient algorithm for obtaining an optimal (control) policy that makes the system have the maximal reliability for every initial state. After establishing the existence of an optimal policy, for the computation of optimal policies, we introduce the concept of an absorbing set of a stationary policy, and find some characterization and a computational method of the absorbing sets. Using the largest absorbing set, we build a novel optimality equation (OE), and prove the uniqueness of a solution of the OE. Furthermore, we provide a policy iteration algorithm of optimal policies, and prove that an optimal policy and the maximal reliability can be obtained in a finite number of iterations. Finally, an example in reliability and maintenance problems is given to illustrate our results.