  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.
