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Detecting optimal and non-optimal actions in average-cost Markov decision processes

Part of: Stochastic systems and control Markov processes

Published online by Cambridge University Press:  14 July 2016

Jean B. Lasserre
Jean B. Lasserre*
Affiliation:
LAAS-CNRS
*
∗ Postal address: LAAS-CNRS, 7 Av. Colonel Roche, 31 077 Toulouse Cedex, France.
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Abstract

We present two sufficient conditions for detection of optimal and non-optimal actions in (ergodic) average-cost MDPs. They are easily interpreted and can be implemented as detection tests in both policy iteration and linear programming methods. An efficient implementation of a recent new policy iteration scheme is discussed.

Keywords

POLICY ITERATIONLINEAR PROGRAMMINGELIMINATION OF NON-OPTIMAL ACTIONS

MSC classification

Primary: 60J10: Markov chains (discrete-time Markov processes on discrete state spaces)
Secondary: 93E20: Optimal stochastic control 90C40: Markov and semi-Markov decision processes

Information

Type
Research Article
Information
Journal of Applied Probability , Volume 31 , Issue 4 , December 1994 , pp. 979 - 990
Copyright
Copyright © Applied Probability Trust 1994 

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References

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