Approximating ergodic average reward continuous: time controlled Markov chains
Loading...
Official URL
Full text at PDC
Publication date
2010
Authors
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Citations
Exportar
Citation
T. Prieto-Rumeau and J. M. Lorenzo, "Approximating Ergodic Average Reward Continuous-Time Controlled Markov Chains," in IEEE Transactions on Automatic Control, vol. 55, no. 1, pp. 201-207, Jan. 2010, doi: 10.1109/TAC.2009.2033848. keywords: {Convergence;Optimal control;State-space methods;Statistics;Operations research;Process control;Adaptive control;Terminology;Approximation of control problems;Ergodic Markov decision processes (MDPs);policy iteration algorithm},
Abstract
We study the approximation of an ergodic average reward continuous-time denumerable state Markov decision process (MDP) by means of a sequence of MDPs. Our results include the convergence of the corresponding optimal policies and the optimal gains. For a controlled upwardly skip-free process, we show some computational results to illustrate the convergence theorems