Continuous control of a polymerization system with deep reinforcement learning

Reinforcement learning is a branch of machine learning, where the machines gradually learn control behaviors via self-exploration of the environment. In this paper, we present a controller using deep reinforcement learning (DRL) with Deep Deterministic Policy Gradient (DDPG) for a non-linear semi-batch polymerization reaction. Several adaptations to apply DRL for chemical process control are addressed in this paper including the Markov state assumption, action boundaries and reward definition. This work illustrates that a DRL controller is capable of handling complicated control tasks for chemical processes with multiple inputs, non-linearity, large time delay and noise tolerance. The application of this Al-based framework, using DRL, is a promising direction in the field of chemical process control towards the goal of smart manufacturing. (C) 2018 Elsevier Ltd. All rights reserved.