Digital Twins and Reinforcement Learning for Autonomous Systems in Industry 4.0: A Comprehensive Survey

Abstract

In this chapter, the authors delve into the compelling intersection of digital twins and reinforcement learning, aiming to propel the autonomy of systems within the Industry 4.0 paradigm. This investigation centers on uncovering the synergistic relationship between these two cutting-edge technologies and their profound implications for industrial settings. By seamlessly integrating digital twins and reinforcement learning, the authors seek to unlock new frontiers in efficiency, adaptability, and decision-making processes. Through an in-depth exploration of this integration, they anticipate shedding light on how these technologies collaboratively contribute to the evolution of smart, autonomous systems in industries. The study not only examines the theoretical framework but also delves into practical applications, aiming to discern the tangible impact on operational efficiency, adaptability to dynamic environments, and the overall decision-making prowess of autonomous systems in the context of Industry 4.0.