A Survey of Reinforcement Learning for Optimization in Automation

TL;DR

This survey reviews the application of reinforcement learning in automation, highlighting its benefits, challenges, and future research directions across manufacturing, energy, and robotics sectors.

Contribution

It provides a comprehensive overview of RL methods in automation, discusses current challenges, and outlines future research pathways, serving as a guide for researchers and practitioners.

Findings

RL enhances optimization in automation sectors.

Major challenges include sample efficiency and safety.

Future research focuses on transfer learning and real-world deployment.

Abstract

Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.