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

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 visi...

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Bibliographic Details
Main Authors: Klaška, David, Kučera, Antonín, Kůr, Vojtěch, Musil, Vít, Řehák, Vojtěch
Format: Conference Proceeding
Language:English
Online Access:Get full text
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Summary: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.
ISSN:2159-5399
2374-3468
DOI:10.1609/aaai.v38i18.29993