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

Authors:
Kazuyoshi Wakuta
Affiliations:
Nagaoka Technical College, Nagaoka, Niigata 940-8532, Japan, E-mail: wakuta@nagaoka-ct.ac.jp
Venue:
Probability in the Engineering and Informational Sciences
Year:
2000

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Abstract

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.