Optimizing Local Satisfaction of Long-Run Average Objectives in Markov Decision Processes
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Proceedings |
| Conference | Proceedings of 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024) |
| MU Faculty or unit | |
| Citation | |
| Web | Paper URL |
| Doi | http://dx.doi.org/10.1609/aaai.v38i18.29993 |
| Keywords | Markov decision processes; invariant distribution |
| Attached files | |
| 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. |
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