0583563 20250131155208.120240304235959.9 6 s. P cav_un_epca*0583562978-80-11-04132-82788-3965378-383PrahaThe Czech Society of Operations Research2023SekničkováJanaHolýVladimír controlled semi-Markov reward processes long-run optimality intervention of the decision maker cav_un_auth*0101196 Sladký Karel UTIA-B Ekonometrie Department of Econometrics E E 100 Ústav teorie informace a automatizace AV ČR, v. v. i. 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. cav_un_auth*0464093 MME 2023: Mathematical Methods in Economics /41./ 20230913 20230915 Prague mme2023.vse.cz CZ BB 10000 10100 10103 2024 1 PR RVO:67985556 https://hdl.handle.net/11104/0351597 S 2023 C SCOPUS PUBMED WOS cav_un_epca*0583562 Proceedings of the 41st International Conference on Mathematical Methods in Econometrics The Czech Society of Operations Research 2023 Praha 378 383 978-80-11-04132-8 2788-3965 Sekničková Jana 340 Holý Vladimír 340