# Deep reinforcement learning for process design: Review and perspective

arXiv: https://arxiv.org/abs/2308.07822 · HTML: https://mlsanity.com/paper/2308.07822/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Molecular De Novo Design through Deep Reinforcement Learning](https://mlsanity.com/paper/1704.07555/) (2017)
- [Bridging the Gap Between Value and Policy Based Reinforcement Learning](https://mlsanity.com/paper/1702.08892/) (2017)
- [ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning](https://mlsanity.com/paper/1605.02097/) (2016)
- [Molecular Hypergraph Grammar with its Application to Molecular Optimization](https://mlsanity.com/paper/1809.02745/) (2018)
- [Molecular Contrastive Learning with Chemical Element Knowledge Graph](https://mlsanity.com/paper/2112.00544/) (2021)
- [Toward automatic generation of control structures for process flow diagrams with large language models](https://mlsanity.com/paper/2211.05583/) (2022)
- [Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning](https://mlsanity.com/paper/2101.04422/) (2021)
- [SFILES 2.0: An extended text-based flowsheet representation](https://mlsanity.com/paper/2208.00778/) (2022)
- [Learning from flowsheets: A generative transformer model for autocompletion of flowsheets](https://mlsanity.com/paper/2208.00859/) (2022)
- [Deep Reinforcement Learning for Process Synthesis](https://mlsanity.com/paper/2009.13265/) (2020)
- [Integrating process design and control using reinforcement learning](https://mlsanity.com/paper/2108.05242/) (2021)
- [Transfer learning for process design with reinforcement learning](https://mlsanity.com/paper/2302.03375/) (2023)
- [Synthesis of separation processes with reinforcement learning](https://mlsanity.com/paper/2211.04327/) (2022)
- [Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning](https://mlsanity.com/paper/2212.04136/) (2022)

## Built on by (most cited first, 10 of 10)

- [Generative AI and Process Systems Engineering: The Next Frontier](https://mlsanity.com/paper/2402.10977/) (2024)
- [Multi-agent systems for chemical engineering: A review and perspective](https://mlsanity.com/paper/2508.07880/) (2025)
- [Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence](https://mlsanity.com/paper/2412.00508/) (2024)
- [Learning to control inexact Benders decomposition via reinforcement learning](https://mlsanity.com/paper/2508.06700/) (2025)
- [Molecular Machine Learning in Chemical Process Design](https://mlsanity.com/paper/2508.20527/) (2025)
- [Federated Learning from Molecules to Processes: A Perspective](https://mlsanity.com/paper/2506.18525/) (2025)
- [Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations](https://mlsanity.com/paper/2603.12813/) (2026)
- [Artificial Liver Classifier: A New Alternative to Conventional Machine Learning Models](https://mlsanity.com/paper/2501.08074/) (2025)
- [Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing](https://mlsanity.com/paper/2605.21213/) (2026)
- [Augmenting PID Control with Deep Reinforcement Learning: A Hybrid Approach to the Industrial Benchmark](https://mlsanity.com/paper/2609.22584/) (2026)

## Abstract (arXiv)

The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial intelligence offer opportunities to accelerate this transition. Specifically, deep reinforcement learning, a subclass of machine learning, has shown the potential to solve complex decision-making problems and aid sustainable process design. We survey state-of-the-art research in reinforcement learning for process design through three major elements: (i) information representation, (ii) agent architecture, and (iii) environment and reward. Moreover, we discuss perspectives on underlying challenges and promising future works to unfold the full potential of reinforcement learning for process design in chemical engineering.