Bias optimality for multichain continuous-time Markov decision processes

Authors:
Xianping Guo;Xinyuan Song;Junyu Zhang
Affiliations:
The School of Mathematics and Computational Science, Zhongshan University, Guangzhou, PR China;Department of Statistics, The Chinese University of Hong Kong, Hong Kong;The School of Mathematics and Computational Science, Zhongshan University, Guangzhou, PR China
Venue:
Operations Research Letters
Year:
2009

Citing 3
Cited 0

Stochastic dynamic programming and the control of queueing systems

Stochastic dynamic programming and the control of queueing systems
Bias Optimality for Continuous-Time Controlled Markov Chains

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A unified approach to Markov decision problems and performance sensitivity analysis with discounted and average criteria: multichain cases

Automatica (Journal of IFAC)

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Abstract

This paper deals with the bias optimality of multichain models for finite continuous-time Markov decision processes. Based on new performance difference formulas developed here, we prove the convergence of a so-called bias-optimal policy iteration algorithm, which can be used to obtain bias-optimal policies in a finite number of iterations.