2024
DOI: 10.1609/aaai.v38i18.29993
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Optimizing Local Satisfaction of Long-Run Average Objectives in Markov Decision Processes

David Klaška,
Antonín Kučera,
Vojtěch Kůr
et al.

Abstract: Long-run average optimization problems for Markov decision processes (MDPs) require constructing policies with optimal steady-state behavior, i.e., optimal limit frequency of visits to the states. However, such policies may suffer from local instability in the sense that the frequency of states visited in a bounded time horizon along a run differs significantly from the limit frequency. In this work, we propose an efficient algorithmic solution to this problem.

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