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arXiv · 2312.12325

Optimizing Local Satisfaction of Long-Run Average Objectives in Markov Decision Processes

David Klaška·Antonín Kučera·Vojtěch Kůr·Vít Musil·Vojtěch Řehák
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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, i.e., 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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David Klaška, Antonín Kučera, Vojtěch Kůr, Vít Musil, Vojtěch Řehák. 2023-12-19. Optimizing Local Satisfaction of Long-Run Average Objectives in Markov Decision Processes. https://arxiv.org/abs/2312.12325

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