  Maintenance scheduling is a complex decision-making problem in the production
domain, where a number of maintenance tasks and resources has to be assigned
and scheduled to production entities in order to prevent unplanned production
downtime. Intelligent maintenance strategies are required that are able to
adapt to the dynamics and different conditions of production systems. The paper
introduces a deep reinforcement learning approach for condition-oriented
maintenance scheduling in flow line systems. Different policies are learned,
analyzed and evaluated against a benchmark scheduling heuristic based on reward
modelling. The evaluation of the learned policies shows that reinforcement
learning based maintenance strategies meet the requirements of the presented
use case and are suitable for maintenance scheduling in the shop floor.
