Researchers from Imperial College London have published a comprehensive survey and tutorial on reinforcement learning (RL) methods tailored for the process systems engineering (PSE) community. The study, authored by Maximilian Bloor, Max Mowbray, Ehecatl Antonio Del Rio Chanona, and Calvin Tsay, aims to bridge the gap between RL and PSE by providing a detailed overview of RL techniques and their applications in process control, optimization, and supply chain management.
The paper begins with a tutorial on fundamental RL concepts, explaining how RL algorithms learn optimal decision-making strategies through interaction with an environment. The authors delve into key algorithmic families, including value-based methods, which estimate the value of actions to determine optimal policies; policy-based methods, which directly optimize the policy; and actor-critic methods, which combine value and policy-based approaches. This foundational knowledge is crucial for PSE practitioners looking to understand how RL can be applied to complex, stochastic systems.
The survey section of the paper explores existing applications of RL in various PSE domains. For instance, in fed-batch and continuous process control, RL has been used to optimize production processes by learning adaptive control policies that respond to real-time data. In process optimization, RL techniques have demonstrated the ability to handle complex, non-linear systems where traditional optimization methods struggle. Additionally, the paper highlights the potential of RL in supply chain management, where it can optimize inventory levels, production schedules, and logistics in the face of uncertainty.
The authors also discuss specialized techniques and emerging directions in RL that are particularly relevant to PSE. They emphasize the importance of sample efficiency, robustness to noise and model inaccuracies, and the ability to handle high-dimensional state and action spaces. These challenges are common in PSE applications, and addressing them is key to the successful implementation of RL in the field.
By synthesizing the current state of RL algorithm development and its implications for PSE, the paper identifies successes, challenges, and trends. It outlines avenues for future research, such as the integration of RL with other machine learning techniques, the development of more robust and interpretable RL algorithms, and the exploration of RL in new PSE applications. The work serves as a valuable resource for researchers and practitioners in PSE, providing a roadmap for leveraging RL to tackle complex decision-making problems in process systems. Read the original research paper here.