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A Survey of Reinforcement Learning for Optimization in Automation

Ahmad Farooq, Kamran Iqbal

TL;DR

This survey addresses optimization in automation via reinforcement learning by analyzing its application to manufacturing, energy systems, and robotics. It synthesizes state-of-the-art RL algorithms and highlights domain-specific achievements, such as DRL/MARL in manufacturing, demand-response and microgrid RL in energy, and motion planning and collaboration RL in robotics. It identifies core challenges—sample efficiency, safety, interpretability, transfer learning, and deployment—and outlines future research directions, including simulation-to-reality transfer and human-in-the-loop deployment. The paper contributes a structured taxonomy, an up-to-date synthesis of methods per domain, and a comprehensive bibliography to guide researchers and practitioners in optimizing industrial systems with RL. Overall, it serves as a foundational resource bridging theory and practice for RL-driven automation.

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.

A Survey of Reinforcement Learning for Optimization in Automation

TL;DR

This survey addresses optimization in automation via reinforcement learning by analyzing its application to manufacturing, energy systems, and robotics. It synthesizes state-of-the-art RL algorithms and highlights domain-specific achievements, such as DRL/MARL in manufacturing, demand-response and microgrid RL in energy, and motion planning and collaboration RL in robotics. It identifies core challenges—sample efficiency, safety, interpretability, transfer learning, and deployment—and outlines future research directions, including simulation-to-reality transfer and human-in-the-loop deployment. The paper contributes a structured taxonomy, an up-to-date synthesis of methods per domain, and a comprehensive bibliography to guide researchers and practitioners in optimizing industrial systems with RL. Overall, it serves as a foundational resource bridging theory and practice for RL-driven automation.

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.

Paper Structure

This paper contains 15 sections, 1 figure, 4 tables.

Table of Contents

  1. INTRODUCTION
  2. Motivation
  3. Scope and Contributions
  4. Organization of the Paper
  5. APPLICATION DOMAINS
  6. Manufacturing
  7. Energy Systems
  8. Robotics
  9. CHALLENGES, STATE OF THE ART, AND FUTURE DIRECTIONS
  10. Sample Efficiency and Scalability
  11. Safety and Robustness
  12. Interpretability and Trustworthiness
  13. Transfer Learning and Meta-learning
  14. Real-world Deployment and Integration
  15. CONCLUSION

Figures (1)

  • Figure 1: Taxonomy of Application Domains of RL for Optimization in Automation