# Deep Reinforcement Learning for Process Control: A Primer for Beginners

arXiv: https://arxiv.org/abs/2004.05490 · HTML: https://mlsanity.com/paper/2004.05490/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning](https://mlsanity.com/paper/2111.07171/) (2021)
- [Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach](https://mlsanity.com/paper/2203.09661/) (2022)
- [Safe Chance Constrained Reinforcement Learning for Batch Process Control](https://mlsanity.com/paper/2104.11706/) (2021)
- [Machine learning for industrial sensing and control: A survey and practical perspective](https://mlsanity.com/paper/2401.13836/) (2024)
- [Hyperparameter Tuning for Deep Reinforcement Learning Applications](https://mlsanity.com/paper/2201.11182/) (2022)
- [Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey](https://mlsanity.com/paper/2209.11123/) (2022)
- [A Meta-Reinforcement Learning Approach to Process Control](https://mlsanity.com/paper/2103.14060/) (2021)
- [Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)](https://mlsanity.com/paper/2508.16474/) (2025)
- [Meta-Reinforcement Learning for Adaptive Control of Second Order Systems](https://mlsanity.com/paper/2209.09301/) (2022)
- [Timing Process Interventions with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2306.04299/) (2023)
- [Analyzing Operator States and the Impact of AI-Enhanced Decision Support in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning Framework for Intervention Strategies](https://mlsanity.com/paper/2402.13219/) (2024)
- [Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control](https://mlsanity.com/paper/2201.03116/) (2022)
- [Reinforcement Learning with Partial Parametric Model Knowledge](https://mlsanity.com/paper/2304.13223/) (2023)
- [Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives](https://mlsanity.com/paper/2404.00247/) (2024)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

Advanced model-based controllers are well established in process industries. However, such controllers require regular maintenance to maintain acceptable performance. It is a common practice to monitor controller performance continuously and to initiate a remedial model re-identification procedure in the event of performance degradation. Such procedures are typically complicated and resource-intensive, and they often cause costly interruptions to normal operations. In this paper, we exploit recent developments in reinforcement learning and deep learning to develop a novel adaptive, model-free controller for general discrete-time processes. The DRL controller we propose is a data-based controller that learns the control policy in real time by merely interacting with the process. The effectiveness and benefits of the DRL controller are demonstrated through many simulations.