# Deep reinforcement learning for process design: Review and perspective

**Authors:** Qinghe Gao, Artur M. Schweidtmann  
**arXiv:** [2308.07822](https://arxiv.org/abs/2308.07822)  
**Date:** 2023-08-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.07822v1/

## Key points

- Authors Qinghe Gao and Artur M. Schweidtmann emphasize the urgent need for new conceptual approaches in chemical process design to support renewable energy and sustainable feedstock supply.
- They highlight the potential of deep reinforcement learning, a subset of machine learning, to accelerate transformation by effectively addressing complex decision-making challenges in chemical engineering.
- The paper explores key elements such as information representation, agent architecture, and environment and reward structures in reinforcement learning for process design.
- Gao and Schweidtmann's analysis provides insights into current applications of reinforcement learning in process design and highlights underlying challenges that need to be addressed.
- The authors propose future research directions to fully utilize reinforcement learning's power in enhancing process design practices within chemical engineering.
- Their work underscores the significant potential of deep reinforcement learning as a tool for driving innovation and sustainability in the chemical industry.

## Summary

In their paper titled "Deep reinforcement learning for process design: Review and perspective," authors Qinghe Gao and Artur M. Schweidtmann emphasize the urgent need for new conceptual approaches in chemical process design to support the shift towards renewable energy and sustainable feedstock supply. They highlight the potential of deep reinforcement learning, a subset of machine learning, to accelerate this transformation by effectively tackling complex decision-making challenges within the field of chemical engineering. Through a comprehensive survey of cutting-edge research on reinforcement learning for process design, Gao and Schweidtmann explore key elements such as information representation, agent architecture, and environment and reward structures. Their analysis not only provides valuable insights into current applications of reinforcement learning in process design but also sheds light on underlying challenges that must be addressed. Additionally, they propose future directions for research to fully harness the power of reinforcement learning in enhancing process design practices within chemical engineering. Overall, Gao and Schweidtmann's work highlights the immense potential of deep reinforcement learning as a tool for driving innovation and sustainability in the chemical industry. By exploring novel approaches and addressing existing limitations, their review sets the stage for further advancements in leveraging artificial intelligence for transformative process design solutions.