@Article{AAM-35-449,
author = {Zhang , Wenzhao and Xiong , Xianzhu},
title = {Convergence of Controlled Models for Continuous-Time Markov Decision Processes with Constrained Average Criteria},
journal = {Annals of Applied Mathematics},
year = {2020},
volume = {35},
number = {4},
pages = {449--464},
abstract = {<p style="text-align: justify;">This paper attempts to study the convergence of optimal values and optimal policies of continuous-time Markov decision processes (CTMDP for short)
under the constrained average criteria. For a given original model $\mathcal{M}$<sub>$∞$</sub> of
CTMDP with denumerable states and a sequence {$\mathcal{M}$<sub style="white-space: normal;">$n$</sub>} of CTMDP with finite states, we give a new convergence condition to ensure that the optimal
values and optimal policies of {$\mathcal{M}$<sub style="white-space: normal;">$n$</sub>} converge to the optimal value and optimal
policy of $\mathcal{M}$<sub style="white-space: normal;">$∞$</sub> as the state space $S$<sub>$n$</sub>&nbsp;of $\mathcal{M}$<sub style="white-space: normal;">$n$</sub> converges to the state space $S$<sub>$∞$</sub>&nbsp;of $\mathcal{M}$<sub>$∞$</sub>, respectively. The transition rates and cost/reward functions of $\mathcal{M}$<sub>$∞$&nbsp;</sub>are allowed to be unbounded. Our approach can be viewed as a combination
method of linear program and Lagrange multipliers.</p>},
issn = {},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/aam/18090.html}
}