David Fridovich-Keil is an assistant professor at the University of Texas at Austin. David’s research spans optimal control, dynamic game theory, learning for control, and robot safety. While he has also worked on problems in distributed control, reinforcement learning, and active search, he is currently investigating the role of dynamic game theory in multi-agent interactive settings such as traffic. David’s work also focuses on the interplay between machine learning and classical ideas from robust, adaptive, and geometric control theory. David completed his PhD under the supervision of Claire Tomlin at UC Berkeley and did a postdoc at Stanford University with Mac Schwager.
This talk introduces dynamic game theory as a natural modeling tool for multi-agent interactions ranging from large, abstract systems such as ride-hailing networks to more concrete, physically-embodied robotic settings such as collision-avoidance in traffic. We present the key theoretical underpinnings of dynamic game models for these varied situations and draw attention to the subtleties of information structure, i.e., what information is implicitly made available to each agent in a game. Thus equipped, the talk presents a state-of-the-art technique for solving these games, as well as a set of “dual” techniques for the inverse problem of identifying players’ objectives based on observations of strategic behavior.