Technical Note—Improved Conditions for Convergence in Undiscounted Markov Renewal Programming | Zendy

Loren K. Platzman | Zendy

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Technical Note—Improved Conditions for Convergence in Undiscounted Markov Renewal Programming

Author(s) -

Loren K. Platzman

Publication year - 1977

Publication title -

operations research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.797

H-Index - 140

eISSN - 1526-5463

pISSN - 0030-364X

DOI - 10.1287/opre.25.3.529

Subject(s) - markov chain , convergence (economics) , markov decision process , mathematical optimization , mathematics , markov process , dynamic programming , state (computer science) , transient (computer programming) , markov renewal process , element (criminal law) , term (time) , computer science , markov model , markov property , algorithm , economics , statistics , physics , quantum mechanics , economic growth , operating system , political science , law

In a simply connected Markov renewal problem, each state is either transient under all policies or an element of a single chain under some policy. This property is easily verified; it implies invariance of the maximal long-term average return (gain) with respect to the initial state, which in turn assures convergence of Odoni's bounds in the damped value-iteration algorithm due to Schweitzer, even when the maximal-gain process is multiple-chained and/or periodic.

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