Average Reward Optimality in Semi-Markov Decision Processes with Costly Interventions

0583563 - ÚTIA 2024 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Sladký, Karel
Average Reward Optimality in Semi-Markov Decision Processes with Costly Interventions.
Proceedings of the 41st International Conference on Mathematical Methods in Econometrics. Praha: The Czech Society of Operations Research, 2023 - (Sekničková, J.; Holý, V.), s. 378-383. ISBN 978-80-11-04132-8. ISSN 2788-3965.
[MME 2023: Mathematical Methods in Economics /41./. Prague (CZ), 13.09.2023-15.09.2023]
Institucionální podpora: RVO:67985556
Klíčová slova: controlled semi-Markov reward processes * long-run optimality * intervention of the decision maker
Obor OECD: Statistics and probability
http://library.utia.cas.cz/separaty/2023/E/sladky-0583563.pdf

In this note we consider semi-Markov reward decision processes evolving on finite state spaces. We focus attention on average reward models, i.e. we establish explicit formulas for the growth rate of the total expected reward. In contrast to the standard models we assume that the decision maker can also change the running process by some (costly) intervention. Recall that the result for optimality criteria for the classical Markov decision chains in discrete and continuous time setting turn out to be a very specific case of the considered model. The aim is to formulate optimality conditions for semi-Markov models with interventions and present algorithmical procedures for finding optimal solutions.
Trvalý link: https://hdl.handle.net/11104/0351597