  Industry 4.0 is driven by demands like shorter time-to-market, mass
customization of products, and batch size one production. Reinforcement
Learning (RL), a machine learning paradigm shown to possess a great potential
in improving and surpassing human level performance in numerous complex tasks,
allows coping with the mentioned demands. In this paper, we present an OPC UA
based Operational Technology (OT)-aware RL architecture, which extends the
standard RL setting, combining it with the setting of digital twins. Moreover,
we define an OPC UA information model allowing for a generalized plug-and-play
like approach for exchanging the RL agent used. In conclusion, we demonstrate
and evaluate the architecture, by creating a proof of concept. By means of
solving a toy example, we show that this architecture can be used to determine
the optimal policy using a real control system.
