Institute of Information Theory and Automation

Publication details

A Counterexample on Sample-Path Optimality in Stable Markov Decision Chains with the Average Reward Criterion

Journal Article

Cavazos-Cadena R., Montes-de-Oca R., Sladký Karel


serial: Journal of Optimization Theory and Applications vol.163, 2 (2014), p. 674-684

project(s): 012/300/02, PSF Organization, 171396, CONACYT (México) and ASCR (Czech Republic)

keywords: Strong sample-path optimality, Lyapunov function condition, Stationary policy, Expected average reward criterion

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abstract (eng):

This note deals with Markov decision chains evolving on a denumerable state space. Under standard continuity compactness requirements, an explicit example is provided to show that, with respect to a strong sample-path average reward criterion, the Lyapunov function condition does not ensure the existence of an optimal stationary policy.

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Last modification: 21.12.2012
Institute of Information Theory and Automation