AI RESEARCH
Optimal Control of Multiclass Fluid Queueing Networks: A Machine Learning Approach
arXiv CS.LG
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ArXi:2307.12405v2 Announce Type: replace We propose a machine learning approach to the optimal control of multiclass fluid queueing networks (MFQNETs) that provides explicit and insightful control policies. We prove that a piecewise constant optimal policy exists for MFQNET control problems, with segments separated by hyperplanes passing through the origin. We use Optimal Classification Trees with hyperplane splits (OCT-H) to set and apply OCT-H to learn explicit control policies.