Numerical Approximations for Discounted Continuous Time Markov Decision Processes
DUFOUR, François
Institut Polytechnique de Bordeaux [Bordeaux INP]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Institut Polytechnique de Bordeaux [Bordeaux INP]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
DUFOUR, François
Institut Polytechnique de Bordeaux [Bordeaux INP]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
< Reduce
Institut Polytechnique de Bordeaux [Bordeaux INP]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Language
en
Chapitre d'ouvrage
This item was published in
Modeling, Stochastic Control, Optimization, and Applications, Modeling, Stochastic Control, Optimization, and Applications. 2019-07-17p. 147-171
Springer
English Abstract
This paper deals with a continuous-time Markov decision process M, with Borel state and action spaces, under the total expected discounted cost optimality criterion. By suitably approximating an underlying probability ...Read more >
This paper deals with a continuous-time Markov decision process M, with Borel state and action spaces, under the total expected discounted cost optimality criterion. By suitably approximating an underlying probability measure with a measure with finite support and by discretizing the action sets of the control model, we can construct a finite state and action space Markov decision process that approximates M and that can be solved explicitly. We can derive bounds on the approximation error of the optimal discounted cost function; such bounds are written in terms of Wasserstein and Hausdorff distances. We show a numerical application to a queueing problem.Read less <
Origin
Hal imported