Reinforcement Learning for Intelligent Engineering Systems: A Comprehensive Review of Applications, Challenges and Future Prospects

Abstract

Reinforcement Learning (RL) is revolutionizing the field of engineering through the solution of challenging, nonlinear, and high-dimensional problems. This review examines how RL enriches the subjects of engineering, such as optimization of industrial processes. Current techniques in optimization and control are inefficient for some complex systems, but RL serves as a better alternative through real-time optimization, product quality improvement, and optimization of process efficiency. The article focuses on recent advancements, challenges, and future prospects for extended integration of RL in engineering and its possibility to revolutionize the field. It also states its limitations and suggestions for future research. The review serves as a good source of information for researchers and engineers determined to remain up to date with recent advancements in RL for intelligent engineering systems and extend its development.