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Direct Sample-Average Learning for Queueing Control: Gradient-Free Top-$K$ Coarse-to-Fine Optimization
We study steady-state queueing control when decisions must be learned from a single persistent operating trajectory: only the implemented action produces data, and the queue is not reset when the action changes. Fixed-event-count cycles yield a direct ratio-of-totals sample-average approximation (SA
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