AI for Control: Reinforcement Learning Reinforcement learning, a subset of artificial intelligence, is a computational approach that models decision-making by exploring the cause-and-effect relationships between actions and rewards. It provides a framework for solving optimization problems where an agent interacts with its environment and refines its policies over time. Closely related to both optimal and adaptive control, reinforcement learning has significant applications in control systems. This study explores the fundamental principles of reinforcement learning and its integration into control applications. Speaker(s): Chang-hee Won, PhD Agenda: WEBINAR: 6:30 – 9:00 P.M. The Zoom Webinar link and password will be forwarded to all registered participants after Noon on the day of the meeting. Check your spam folder if you don’t see the email. PDH certificates are available and an evaluation form will be emailed to you after the meeting. PDH certificate are sent by IEEE USA 3-4 weeks after the meeting. Virtual: https://events.vtools.ieee.org/m/467824