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. Nevertheless, 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.

本研究利用深度强化学习在 Job-Shop Scheduling 中设计了高效的 DRL 环境，使用紧密联系于 COP 方法的稀疏最小化的准则的新型简单而密集的奖励函数，相对于现有的 DRL 方法在经典基准实例上表现出更好的性能，接近先进的 COP 方法。