mlsanityData to 7 Oct 2026

A Survey of Reinforcement Learning for Optimization in Automation

arXiv 2502.09417 ↗First posted 13 Feb 2025cs.LG cs.AI cs.NEcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 36 of its 96 references that are in the map. First posted on arXiv 13 Feb 2025.

Builds on

36 of 96 references.

References · 36

  1. 2020Meta-Learning in Neural Networks: A Surveycited by 944
  2. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  3. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 350
  4. 2023Efficient Online Reinforcement Learning with Offline Datacited by 332
  5. 2017Deep Reinforcement Learning: An Overviewcited by 327
  6. 2020Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimizationcited by 120
  7. 2020Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Modelcited by 108
  8. 2020Safe Reinforcement Learning via Curriculum Inductioncited by 73
  9. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  10. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 44
  11. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  12. 2022On the Robustness of Safe Reinforcement Learning under Observational Perturbationscited by 38
24 more
  1. 2020Robotic Grasping using Deep Reinforcement Learningcited by 27
  2. 2021Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Criticcited by 23
  3. 2020Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chainscited by 13
  4. 2020Human-centered collaborative robots with deep reinforcement learningcited by 12
  5. 2023Safe Reinforcement Learning with Dual Robustnesscited by 10
  6. 2023Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guaranteescited by 9
  7. 2020Real-World Human-Robot Collaborative Reinforcement Learningcited by 8
  8. 2021Measuring Progress in Deep Reinforcement Learning Sample Efficiencycited by 8
  9. 2021Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programmingcited by 8
  10. 2023Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimizationcited by 8
  11. 2020Falsification-Based Robust Adversarial Reinforcement Learningcited by 6
  12. 2022Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learningcited by 5
  13. 2022Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Taskscited by 5
  14. 2023MARLIM: Multi-Agent Reinforcement Learning for Inventory Managementcited by 5
  15. 2021Reinforcement Learning for Process Control with Application in Semiconductor Manufacturingcited by 4
  16. 2022There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazescited by 4
  17. 2021Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Responsecited by 3
  18. 2022Distributional Reinforcement Learning for Scheduling of Chemical Production Processescited by 2
  19. 2023Deploying Offline Reinforcement Learning with Human Feedbackcited by 2
  20. 2023Cooperative Multi-Agent Reinforcement Learning for Inventory Managementcited by 2
  21. 2023Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challengescited by 2
  22. 2021Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific researchcited by 1
  23. 2022Dext-Gen: Dexterous Grasping in Sparse Reward Environments with Full Orientation Controlcited by 1
  24. 2022Reinforcement Learning Based User-Guided Motion Planning for Human-Robot Collaborationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Residual Off-Policy RL for Finetuning Behavior Cloning Policiescited by 42
  2. 2025Scalable Multi-Objective Robot Reinforcement Learning through Gradient Conflict Resolutioncited by 4
  3. 2025Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environmentscited by 3
  4. 2026Interpretable experiential learning based on state history and global feedbackcited by 0