Given the fact that some products in the production system require larger cycle times and a setup process during production, the application of different lot sizes might be beneficial regarding economic and logistic key performance indicators. Maximizing the profit considering the according work in process, inventory, set up and penalty costs is a complex challenge. In this contribution, we present the complete workflow from collecting data, deriving a simulation model, as well as training and deploying a machine learning model for assigning lot sizing in a dynamic production system. Furthermore, we compare the results of human participants and the reinforcement learning agent in a real-world learning factory workshop. The comparison of the results shows, that the reinforcement learning-agent is able to achieve the same level of results as the human experts.