“Reinforcement Learning – Overview of Recent Progress and Potential Applications for Process Systems Engineering”
This seminar provides a brief introduction to Reinforcement Learning (RL) technology, summarizes recent developments in this area, and discusses their potential implications for the field of process systems engineering. The paper begins with a brief introduction to RL, a machine learning technology that allows an agent to learn, through trial and error, the best way to accomplish a task. We then highlight two new developments in RL that have led to the recent wave of applications and media interest. A comparison of the key features of RL vs. Model Predictive Control (MPC) and other traditional mathematical programming based methods is then presented in order to clarify their relative merits and shortcomings. This is followed by an assessment of areas that RL technology can potentially be used in process systems engineering applications. Particular focus is given on integrating planning and scheduling layers in multi-scale, multi-period, stochastic problems.
Date(s) - Dec 14, 2018
10:00 am - 11:00 am
The Penthouse, 8500 Boelter
Boelter Hall, UCLA, Los Angeles