Reinforcement Learning for Autonomous Process Control in Industry 4.0: Advantages and Challenges

Abstract

In recent years, the integration of intelligent industrial process monitoring, quality prediction, and predictive maintenance solutions has garnered significant attention, driven by rapid advancements in digitalization, data analytics, and machine learning. As traditional production systems evolve into self-aware and self-learning configurations, capable of autonomously adapting to dynamic environmental and production conditions, the significance of reinforcement learning becomes increasingly apparent. This paper provides an overview of reinforcement learning developments and applications in the manufacturing industry. Various sectors within manufacturing, including robot automation, welding processes, the semiconductor industry, injection molding, metal forming, milling processes, and the power industry, are explored for instances of reinforcement learning application. The analysis focuses on application types, problem modeling, training algorithms, validation methods, and deployment statuses. Key benefits of reinforcement learning in these applications are identified. Particular emphasis is placed on elucidating the primary obstacles impeding the adoption and implementation of reinforcement learning technology in industrial settings, such as model complexity, accessibility to simulation environments, safety deployment constraints, and model interpretability. The paper concludes by proposing potential alternatives and avenues for future research to address these challenges, including improving sample efficiency and bridging the simulation-to-reality gap.