Multiple Objective Nonatomic Markov Decision Processes with Total Reward Criteria

Abstract

We consider a Markov decision process with an uncountable state space and multiple rewards. For each policy, its performance is evaluated by a vector of total expected rewards. Under the standard continuity assumptions and the additional assumption that all initial and transition probabilities are nonatomic, we prove that the set of performance vectors for all policies is equal to the set of performance vectors for (nonrandomized) Markov policies. This result implies the existence of optimal (nonrandomized) Markov policies for nonatomic constrained Markov decision processes with total rewards. We provide two examples of applications of our results to constrained multiple objective problems in inventory control and finance.