The importance of job shop production is increasing in order to meet the customer-driven greater demandfor products with a larger number of variants in small quantities. However, it also leads to higherrequirements for the production planning and control. In order to meet logistical target values and customerneeds, one approach is the focus on dynamic planning systems, which can reduce ad-hoc controlinterventions in the running production. In particular, the release of orders at the beginning of the productionprocess has a high influence on the planning quality. Previous approaches used advanced methods such ascombinations of reinforcement learning (RL) and simulation to improve specific production environments,which are sometimes highly simplified and not practical enough. This paper presents a practice-basedapplication of an automated order release procedure based on RL using the example of real-world productionscenarios. Both, the training environment, and the data processing method are introduced. Primarily, threeaspects to achieve a higher practical orientation are addressed: A more realistic problem size compared toprevious approaches, a higher customer orientation by means of an objective regarding adherence to deliverydate and a control application for development and performance evaluation of the considered algorithmsagainst known order release strategies. Follow-up research will refine the objective function, continue toscale-up the problem size and evaluate the algorithm’s scheduling results in case of changes in the system.