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Main Authors:	Klaška, David, Kučera, Antonín, Kůr, Vojtěch, Musil, Vít, Řehák, Vojtěch
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2412.13369
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author	Klaška, David Kučera, Antonín Kůr, Vojtěch Musil, Vít Řehák, Vojtěch
author_facet	Klaška, David Kučera, Antonín Kůr, Vojtěch Musil, Vít Řehák, Vojtěch
contents	The long-run average payoff per transition (mean payoff) is the main tool for specifying the performance and dependability properties of discrete systems. The problem of constructing a controller (strategy) simultaneously optimizing several mean payoffs has been deeply studied for stochastic and game-theoretic models. One common issue of the constructed controllers is the instability of the mean payoffs, measured by the deviations of the average rewards per transition computed in a finite "window" sliding along a run. Unfortunately, the problem of simultaneously optimizing the mean payoffs under local stability constraints is computationally hard, and the existing works do not provide a practically usable algorithm even for non-stochastic models such as two-player games. In this paper, we design and evaluate the first efficient and scalable solution to this problem applicable to Markov decision processes.
format	Preprint
id	arxiv_https___arxiv_org_abs_2412_13369
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Multiple Mean-Payoff Optimization under Local Stability Constraints Klaška, David Kučera, Antonín Kůr, Vojtěch Musil, Vít Řehák, Vojtěch Artificial Intelligence The long-run average payoff per transition (mean payoff) is the main tool for specifying the performance and dependability properties of discrete systems. The problem of constructing a controller (strategy) simultaneously optimizing several mean payoffs has been deeply studied for stochastic and game-theoretic models. One common issue of the constructed controllers is the instability of the mean payoffs, measured by the deviations of the average rewards per transition computed in a finite "window" sliding along a run. Unfortunately, the problem of simultaneously optimizing the mean payoffs under local stability constraints is computationally hard, and the existing works do not provide a practically usable algorithm even for non-stochastic models such as two-player games. In this paper, we design and evaluate the first efficient and scalable solution to this problem applicable to Markov decision processes.
title	Multiple Mean-Payoff Optimization under Local Stability Constraints
topic	Artificial Intelligence
url	https://arxiv.org/abs/2412.13369

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