Multiple Mean-Payoff Optimization under Local Stability Constraints

• URL: http://arxiv.org/abs/2412.13369v1
• Date: Tue, 17 Dec 2024 22:53:08 GMT
• Title: Multiple Mean-Payoff Optimization under Local Stability Constraints
• Authors: David Klaška, Antonín Kučera, Vojtěch Kůr, Vít Musil, Vojtěch Řehák,
• Abstract summary: 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 and game-theoretic models. In this paper, we design and evaluate the first efficient and scalable solution to this problem applicable to Markov decision processes.
• Score: 2.95348334737984
• License:
• Abstract: 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.

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