Reinforcement learning for industrial process control

Funding Information: This work was supported by China Scholarship Council (No. 202006080008 ), the National Natural Science Foundation of China (Grant Nos. 52074085 and U21A20117 ), the Fundamental Research Funds for the Central Universities (Grant No. N2004010 ), and the LiaoNing Revitalization Talents Program ( XLYC1907065 ). Publisher Copyright: © 2022 The AuthorsStrip rolling is a typical manufacturing process, in which conventional control approaches are widely applied. Development of the control algorithms requires a mathematical expression of the process by means of the first principles or empirical models. However, it is difficult to upgrade the conventional control approaches in response to the ever-changing requirements and environmental conditions because domain knowledge of control engineering, mechanical engineering, and material science is required. Reinforcement learning is a machine learning method that can make the agent learn from interacting with the environment, thus avoiding the need for the above mentioned mathematical expression. This paper proposes a novel approach that combines ensemble learning with reinforcement learning methods for strip rolling control. Based on the proximal policy optimization (PPO), a multi-actor PPO is proposed. Each randomly initialized actor interacts with the environment in parallel, but only the experience from the actor that obtains the highest reward is used for updating the actors. Simulation results show that the proposed method outperforms the conventional control methods and the state-of-the-art reinforcement learning methods in terms of process capability and smoothness.Peer reviewe