Safe Learning for Near Optimal Scheduling
Gilles Geeraerts, Shibashis Guha, Guillermo A. Pérez, Jean-François Raskin · arXiv (Cornell University) · 2020
In this paper, we investigate the combination of synthesis techniques and learning techniques to obtain safe and near optimal schedulers for a preemptible task scheduling problem. We study both model-based learning techniques with PAC guarantees and model-free learning techniques based on shielded deep Q-learning. The new learning algorithms have been implemented to conduct experimental evaluations.