---
res:
bibo_abstract:
- We study controller synthesis problems for finite-state Markov decision processes,
where the objective is to optimize the expected mean-payoff performance and stability
(also known as variability in the literature). We argue that the basic notion
of expressing the stability using the statistical variance of the mean payoff
is sometimes insufficient, and propose an alternative definition. We show that
a strategy ensuring both the expected mean payoff and the variance below given
bounds requires randomization and memory, under both the above definitions. We
then show that the problem of finding such a strategy can be expressed as a set
of constraints.@eng
bibo_authorlist:
- foaf_Person:
foaf_givenName: Tomáš
foaf_name: Brázdil, Tomáš
foaf_surname: Brázdil
- foaf_Person:
foaf_givenName: Krishnendu
foaf_name: Chatterjee, Krishnendu
foaf_surname: Chatterjee
foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0002-4561-241X
- foaf_Person:
foaf_givenName: Vojtěch
foaf_name: Forejt, Vojtěch
foaf_surname: Forejt
- foaf_Person:
foaf_givenName: Antonín
foaf_name: Kučera, Antonín
foaf_surname: Kučera
bibo_doi: 10.1016/j.jcss.2016.09.009
bibo_volume: 84
dct_date: 2017^xs_gYear
dct_language: eng
dct_publisher: Elsevier@
dct_title: Trading performance for stability in Markov decision processes@
...