A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications
This is an incremental application of existing RL methods to a specific industrial use case for Industry 4.0, with no broad impact claimed.
The paper tackles the problem of applying reinforcement learning to industrial tasks like transporting and assembling goods in a model factory, aiming to improve production efficiency, but does not report specific numerical results.
This application paper explores the potential of using reinforcement learning (RL) to address the demands of Industry 4.0, including shorter time-to-market, mass customization, and batch size one production. Specifically, we present a use case in which the task is to transport and assemble goods through a model factory following predefined rules. Each simulation run involves placing a specific number of goods of random color at the entry point. The objective is to transport the goods to the assembly station, where two rivets are installed in each product, connecting the upper part to the lower part. Following the installation of rivets, blue products must be transported to the exit, while green products are to be transported to storage. The study focuses on the application of reinforcement learning techniques to address this problem and improve the efficiency of the production process.