Počet záznamů: 1
Second Order Optimality in Markov Decision Chains
- 1.
SYSNO ASEP 0485146 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Second Order Optimality in Markov Decision Chains Tvůrce(i) Sladký, Karel (UTIA-B) RID Celkový počet autorů 1 Zdroj.dok. Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954
Roč. 53, č. 6 (2017), s. 1086-1099Poč.str. 14 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova Markov decision chains ; second order optimality ; optimalilty conditions for transient, discounted and average models ; policy and value iterations Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability CEP GA15-10331S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 UT WOS 000424732300008 EID SCOPUS 85040739483 DOI 10.14736/kyb-2017-6-1086 Anotace The article is devoted to Markov reward chains in discrete-time setting with finite state spaces. Unfortunately, the usual optimization criteria examined in the literature on Markov decision chains, such as a total discounted, total reward up to reaching some specific state (called the first passage models) or mean (average) reward optimality, may be quite insufficient to characterize the problem from the point of a decision maker. To this end it seems that it may be preferable if not necessary to select more sophisticated criteria that also reflect variability -risk features of the problem. Perhaps the best known approaches stem from the classical work of Markowitz on mean variance selection rules, i.e. we optimize the weighted sum of average or total reward and its variance. The article presents explicit formulae for calculating the variances for transient and discounted models (where the value of the discount factor depends on the current state and action taken) for finite and infinite time horizon. The same result is presented for the long run average nondiscounted models where finding stationary policies minimizing the average variance in the class of policies with a given long run average reward is discussed. Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2018
Počet záznamů: 1