The Current Status and Prospects of Reinforcement Learning in the Field of Automation Control

Abstract

The implementation of Industry 4.0 and Intelligent Manufacturing 2035 plan promotes the transformation of the automation control field from traditional fixed strategies to autonomous and adaptive control. Reinforcement learning relies on the core advantages of trial and error learning and online adaptation to address the control challenges of nonlinear and uncertain systems, becoming a key support for technological upgrades in this field. This article combines authoritative industry data and practical application cases to systematically summarize its application status, analyze the adaptability, application scenarios, technological breakthroughs, and existing bottlenecks of automation control, explore the constraints of large-scale applications, and look forward to development trends. Research has found that reinforcement learning has been implemented in control scenarios such as industrial processes, robots, and intelligent energy. However, issues such as sample efficiency, robustness, and implementation costs still constrain its large-scale promotion. In the future, with the deep integration of algorithm optimization and industry, it will promote the development of automation control towards higher levels of intelligence.