Optimization and innovation practice of industrial intelligent system based on deep reinforcement learning

As an essential component of modern industrial development, industrial intelligent systems face the challenge of complex and ever-changing industrial environments. Deep reinforcement learning, by integrating the perceptual capabilities of deep learning with the decision-making abilities of reinforcement learning, offers a revolutionary optimization design approach for industrial intelligent systems. This paper introduces the basic principles and key technologies of deep reinforcement learning, including the Q-learning algorithm, deep Q-network (DQN), and policy gradient algorithms. It then presents specific case studies demonstrating the application of deep reinforcement learning in the optimization design of industrial intelligent systems, such as dynamic resource allocation optimization, dynamic production line scheduling, and intelligent quality control. Additionally, it highlights an innovative practice where a large steel company has used deep reinforcement learning to build an intelligent decision-making system, achieving global optimization in production scheduling, quality control, equipment maintenance, and supply chain management.