AI for Control: Reinforcement Learning Short Course 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. Upon completion of this program, you’ll walk away with: – A solid understanding of reinforcement learning fundamentals as a subset of artificial intelligence – The relationship between artificial intelligence, reinforcement learning, and optimal control. – Practical skills in designing, training, and deploying reinforcement learning-based controllers for dynamic systems. – The ability to understand the value-based reinforcement learning methods for optimization. – The confidence to apply reinforcement learning techniques to real-world control problems in robotics, automation, and beyond. Co-sponsored by: IEEE CSS/CASS/SMCS Philadelphia Chapter 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