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Three common reinforcement learning tricks: when and why do they work



Three common reinforcement learning tricks: when and why do they work


He Niao - ETH Zurich


Reinforcement learning has achieved remarkable breakthroughs recently for outperforming humans in many challenging tasks. Behind the scenes lies in the integration of various algorithmic techniques: neural function approximation, double learning, entropy regularization, etc. This talk will unveil some of the mysteries behind these techniques from theoretical perspectives, by understanding the asymptotic and finite-time behaviors of the algorithm dynamics.


Niao He is currently an Assistant Professor in the Department of Computer Science at ETH Zurich, where she leads the Optimization and Decision Intelligence (ODI) Group. She is also an ELLIS Scholar and a core faculty member of ETH AI Center, ETH-Max Planck Center of Learning Systems, and ETH Foundations of Data Science. Previously, she was an assistant professor at the University of Illinois at Urbana-Champaign from 2016 to 2020. Before that, she received her Ph.D. degree in Operations Research from Georgia Institute of Technology in 2015. Her research interests are in optimization, machine learning, and reinforcement learning.


2021-11-24 at 2:30 pm


Remote, @UniGE