Abstract
Scheduling is a fundamental task occurring in various automated systems applications, e.g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste. However, finding such schedules is often intractable and cannot be achieved by Combinatorial Optimization Problem (COP) methods within a given time limit. Recent advances of Deep Reinforcement Learning (DRL) in learning complex behavior enable new COP application possibilities. This paper presents an efficient DRL environment for Job-Shop Scheduling – an important problem in the field. Furthermore, we design a meaningful and compact state representation as well as a novel, simple dense reward function, closely related to the sparse make-span minimization criteria used by COP methods.
We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances, coming close to state-of-the-art COP approaches.
We demonstrate that our approach significantly outperforms existing DRL methods on classic benchmark instances, coming close to state-of-the-art COP approaches.
Originalsprache | englisch |
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Publikationsstatus | Veröffentlicht - Aug. 2021 |
Veranstaltung | 2021 PRL Workshop – Bridging the Gap Between AI Planning and Reinforcement Learning - Virtuell, China Dauer: 5 Aug. 2021 → 6 Aug. 2021 |
Konferenz
Konferenz | 2021 PRL Workshop – Bridging the Gap Between AI Planning and Reinforcement Learning |
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Land/Gebiet | China |
Ort | Virtuell |
Zeitraum | 5/08/21 → 6/08/21 |