An Architecture for Deploying Reinforcement Learning in Industrial Environments
This work addresses the problem of adapting RL for Industry 4.0 applications, but it is incremental as it builds on existing RL and digital twin concepts with a specific architectural extension.
The paper tackles the challenge of deploying reinforcement learning in industrial settings by proposing an OPC UA-based architecture that integrates with digital twins, and demonstrates its feasibility through a proof-of-concept toy example showing it can determine optimal policies in real control systems.
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.