Control (management)

Process (computing)

Engineering

Control engineering

Stability (learning theory)

Operating system

Computer science

Manufacturing engineering

Process control

Reinforcement learning

Industrial engineering

Artificial intelligence

Structural engineering

Reinforcement

Machine learning

Computers & Chemical Engineering

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 provides an overview of RL along with tutorials for practitioners who are interested in implementing RL solutions into process control applications. The paper starts by providing an introduction to different reinforcement learning algorithms. Then, recent successes of RL applications across different industries will be explored, with more emphasis on process control applications. A detailed RL implementation example will also be shown. Afterwards, RL will be compared with traditional optimal control methods, in terms of stability and computational complexity among other factors, and the current shortcomings of RL will be introduced. This paper is concluded with a summary of RL’s potential advantages and disadvantages.

A review On reinforcement learning: Introduction and applications in industrial process control