Advanced model‐based controllers are well established in process industries. However,
such controllers require regular maintenance to maintain acceptable performance. It is a …

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 …

Advanced model-based control methods have been widely used in industrial process
control, but excellent performance requires regular maintenance of its model. Reinforcement …

Deep reinforcement learning (RL) is an optimization-driven framework for producing control
strategies for general dynamical systems without explicit reliance on process models. Good …

This paper presents a literature review of reinforcement learning (RL) and its applications to
process control and optimization. These applications were evaluated from a new …

Abstract Machine learning (ML) has recently gained in popularity, spurred by well-publicized
advances like deep learning and widespread commercial interest in big data analytics …

In this work, we have extended the current success of deep learning and reinforcement
learning to process control problems. We have shown that if reward hypothesis functions are …

This paper explores a reinforcement learning (RL) approach that designs automatic control
strategies in a large-scale chemical process control scenario as the first step for leveraging …

In recent years, reinforcement learning (RL) has attracted significant attention from both
industry and academia due to its success in solving some complex problems. This paper …

Technical process control is a highly interesting area of application serving a high practical
impact. Since classical controller design is, in general, a demanding job, this area …