par Busatto-Gaston, Damien 
;Chakraborty, Debraj ;Guha, Shibashis ;Perez, Guillermo A. ;Raskin, Jean-François
Référence International Conference on Quantitative Evaluation of Systems
Publication Publié, s.d.
;Chakraborty, Debraj ;Guha, Shibashis ;Perez, Guillermo A. ;Raskin, Jean-François Référence International Conference on Quantitative Evaluation of Systems
Publication Publié, s.d.
Publication dans des actes
| Résumé : | In this paper, we investigate the combination of synthesis, model-based learning, and online sampling techniques to obtain safe and near-optimal schedulers for a preemptible task scheduling problem. Our algorithms can handle Markov decision processes (MDPs) that have 10 20 states and beyond which cannot be handled with state-of-the art probabilistic model-checkers. We provide probably approximately correct (PAC) guarantees for learning the model. Additionally, we extend Monte-Carlo tree search with advice, computed using safety games or obtained using the earliest-deadline-first scheduler, to safely explore the learned model online. Finally, we implemented and compared our algorithms empirically against shielded deep Q-learning on large task systems. |
