AI for Control: Reinforcement Learning

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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.


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  • Date: 23 Apr 2025
  • Time: 06:30 PM to 09:00 PM
  • All times are (GMT-05:00) US/Eastern
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  • Starts 21 February 2025 12:00 AM
  • Ends 23 April 2025 12:00 PM
  • All times are (GMT-05:00) US/Eastern
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  Speakers

Chang-hee Won, PhD of Temple University, Department of Electrical and Computer Engineering

Topic:

AI for Control: Reinforcement Learning

Biography:

Chang-hee Won is a Professor in the Department of Electrical and Computer Engineering and the Director of Control, Sensor, Network, and Perception (CSNAP) Laboratory at Temple University, Philadelphia. He is a topic editor of the IEEE Sensors Journal. Currently, he is actively guiding various research projects funded by the National Science Foundation, Pennsylvania Department of Health, and the Department of Defense. He published over a hundred and twenty peer-reviewed articles. His research interests include tactile sensing, statistical optimal control, dynamic interrogation, and reinforcement learning.

Address:United States




Agenda

WEBINAR: 6:30 - 9:00 P.M. 

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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.