An Architecture for Deploying Reinforcement Learning in Industrial Environments

Author:

Georg Schäfer, Reuf Kozlica, Stefan Wegenkittl, Stefan Huber

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Systems and Control (eess.SY)

journal:

Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2022. EUROCAST 2022. Lecture Notes in Computer Science, vol 13789. Springer, Cham

date:

2023-06-01 16:00:00

Abstract

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.

PDF: An Architecture for Deploying Reinforcement Learning in Industrial Environments.pdf