VECTOR-VALUED MARKOV DECISION PROCESSES WITH AVERAGE REWARD CRITERION: THE MULTICHAIN CASE

摘要

We study the multichain case of a vector-valued Markov decision process with average reward criterion. We characterize optimal deterministic stationary policies via systems of linear inequalities and discuss a policy iteration algorithm for finding all optimal deterministic stationary policies.