# A Survey of Reinforcement Learning for Optimization in Automation

arXiv: https://arxiv.org/abs/2502.09417 · HTML: https://mlsanity.com/paper/2502.09417/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 36 of its 96 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 30 of 36)

- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Efficient Online Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2302.02948/) (2023)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization](https://mlsanity.com/paper/2006.03647/) (2020)
- [Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2005.12900/) (2020)
- [Safe Reinforcement Learning via Curriculum Induction](https://mlsanity.com/paper/2006.12136/) (2020)
- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Safe Reinforcement Learning Using Robust Control Barrier Functions](https://mlsanity.com/paper/2110.05415/) (2021)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [On the Robustness of Safe Reinforcement Learning under Observational Perturbations](https://mlsanity.com/paper/2205.14691/) (2022)
- [Robotic Grasping using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.04499/) (2020)
- [Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic](https://mlsanity.com/paper/2112.10504/) (2021)
- [Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply Chains](https://mlsanity.com/paper/2006.04037/) (2020)
- [Human-centered collaborative robots with deep reinforcement learning](https://mlsanity.com/paper/2007.01009/) (2020)
- [Safe Reinforcement Learning with Dual Robustness](https://mlsanity.com/paper/2309.06835/) (2023)
- [Real-World Human-Robot Collaborative Reinforcement Learning](https://mlsanity.com/paper/2003.01156/) (2020)
- [Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees](https://mlsanity.com/paper/2301.13375/) (2023)
- [Measuring Progress in Deep Reinforcement Learning Sample Efficiency](https://mlsanity.com/paper/2102.04881/) (2021)
- [Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming](https://mlsanity.com/paper/2106.11417/) (2021)
- [Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization](https://mlsanity.com/paper/2307.03860/) (2023)
- [Falsification-Based Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/2007.00691/) (2020)
- [MARLIM: Multi-Agent Reinforcement Learning for Inventory Management](https://mlsanity.com/paper/2308.01649/) (2023)
- [Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning](https://mlsanity.com/paper/2205.14410/) (2022)
- [Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks](https://mlsanity.com/paper/2207.09071/) (2022)
- [Reinforcement Learning for Process Control with Application in Semiconductor Manufacturing](https://mlsanity.com/paper/2110.11572/) (2021)
- [There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes](https://mlsanity.com/paper/2206.04266/) (2022)
- [Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response](https://mlsanity.com/paper/2104.14670/) (2021)
- [Distributional Reinforcement Learning for Scheduling of Chemical Production Processes](https://mlsanity.com/paper/2203.00636/) (2022)

## Built on by (most cited first, 4 of 4)

- [Residual Off-Policy RL for Finetuning Behavior Cloning Policies](https://mlsanity.com/paper/2509.19301/) (2025)
- [Scalable Multi-Objective Robot Reinforcement Learning through Gradient Conflict Resolution](https://mlsanity.com/paper/2509.14816/) (2025)
- [Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environments](https://mlsanity.com/paper/2507.00762/) (2025)
- [Interpretable experiential learning based on state history and global feedback](https://mlsanity.com/paper/2605.00940/) (2026)

## 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.