Recent Advances in Reinforcement Learning for Chemical Process Control

Abstract

This paper reviews the recent advancements of reinforcement learning (RL) for chemical process control. RL presents a systematic strategy in which the machine learning agent learns a policy of actions based on interactions with the environment. We first provide a brief overview of RL theoretic basis built on Markov decision processes (MDPs) and then move onto its application to process control. With particular interest in chemical processes, we review state-of-the-art research developments on RL for controller tuning and direct control policy learning. This work highlights the importance of safe RL control to incorporate deterministic or probabilistic safety constraints such as constrained MDPs, control barrier functions, etc. We conclude the review with a discussion on some of the outstanding challenges such as sampling efficiency, generalizability, uncertainty, and observability, as well as the emergent and future directions to address these limitations.