Benjamin Eysenbach
Princeton University
Seminar Information
Empowerment measures an agent's ability to actively control its environment. Appealing as an information theoretic quantity, empowerment is often juxtaposed with more geometric approaches to exploration, such as those that build a 3D model of the world. In this talk, I'll discuss recent results linking empowerment and geometry, which provides an answer to a longstanding open question on the connections between empowerment and centrality. These results suggest new approaches to representation learning, exploration, and information gathering, which we demonstrate on robotics benchmarks and open-ended games.
Benjamin Eysenbach is an Assistant Professor of Computer Science at Princeton University, where he runs the Princeton Reinforcement Learning Lab. His research focuses on reinforcement learning algorithms: AI methods that learn how to make intelligent decisions from trial and error. His group has developed several successful algorithms and analysis for self-supervised methods, which enable agents to explore and learn without any human supervision. His work has been recognized by a NeurIPS Best Paper Award, an NSF CAREER Award, a Sloan Fellowship, a Hertz Fellowship, an NSF GRFP Fellowship, the Alfred Rheinstein Faculty Award, as well as several teaching awards.