Publication details

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


KLAŠKA David KUČERA Antonín KŮR Vojtěch MUSIL Vít ŘEHÁK Vojtěch

Year of publication 2024
Type Article in Proceedings
Conference Proceedings of 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024)
MU Faculty or unit

Faculty of Informatics

Web Paper URL
Keywords Markov decision processes; invariant distribution
Description 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.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info