Optimizing Industrial Systems Through Deep Q-Networks and Proximal Policy Optimization in Reinforcement Learning

Abstract

This research paper explores the application of advanced reinforcement learning techniques, specifically Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to optimize industrial systems. These methodologies are evaluated for their effectiveness in enhancing operational efficiency, minimizing resource consumption, and improving decision-making processes in complex industrial environments. The study begins by outlining the limitations of traditional optimization approaches and the potential advantages of integrating reinforcement learning. We present an in-depth comparison between DQN and PPO, focusing on their architectures, convergence rates, and adaptability to dynamic industrial scenarios. A series of experiments were conducted, simulating real-world industrial processes, to assess the performance of these algorithms in scenarios such as energy management, supply chain optimization, and predictive maintenance. Results indicate that DQNs provide robust solutions in environments with discrete action spaces, while PPO demonstrates superior performance in continuous action spaces, offering better stability and policy improvement. Furthermore, a hybrid approach is proposed to leverage the strengths of both techniques, resulting in a significant increase in system efficiency compared to traditional methods. The findings suggest that incorporating these cutting-edge reinforcement learning strategies can lead to transformative improvements in industrial systems, paving the way for more autonomous and intelligent operations. The paper concludes by discussing the practical implications, potential challenges, and future research directions in deploying DQN and PPO in industrial settings.