An AI-Gym for Industry 4.0
Belal Abu Naim, Mojtaba Zahedi Amiri, Markus Tauber · 2025
The rapid evolution of Industry 4.0 demands advanced solutions for developing and deploying AI models that can interact seamlessly with industrial IoT (IIoT) systems. A critical challenge lies in creating a controlled, efficient environment to develop, train, and test AI models using real-world data streams before deploying them in production. To address this, we propose an AI-gym framework that integrates Apache Zeppelin, Eclipse Arrowhead, and IIoT components to facilitate real-time model development and validation. This approach bridges the gap between theoretical model design and practical deployment, enabling iterative experimentation with live IIoT data from industrial setups. To demonstrate its effectiveness, we validated the framework with a use case in Controlled Environment Agriculture (CEA), optimizing climate control using live data streams from sensors and actuators. By enabling real-time interaction, seamless IIoT integration, and robust testing, the proposed AI-gym aligns with the principles of Industry 4.0, such as digitalization, smart manufacturing, and data-driven decision-making, offering a transformative tool for industrial AI applications.