Safe and Efficient Exploration in Reinforcement Learning

Time: June 18th, 2021 15:00
Locaiton: N412, Mong Man-wei Science Technology Building

At the heart of Reinforcement Learning lies the challenge of trading exploration -- collecting data for identifying better models -- and exploitation -- using the estimate to make decisions.  In simulated environments (e.g., games), exploration is primarily a computational concern.  In real-world settings, exploration is costly, and a potentially dangerous proposition, as it requires experimenting with actions that have unknown consequences.  In this talk, I will present our work towards improving efficiency and rigorously reasoning about safety of exploration in reinforcement learning.  I will discuss approaches, where we learn about unknown system dynamics through exploration, yet need to verify safety of the estimated policy.  Our approaches use Bayesian inference over the objective, constraints and dynamics, and -- under some regularity conditions -- are guaranteed to be both safe and complete, i.e., converge to a natural notion of reachable optimum. I will also present recent results on harnessing uncertainty for improving efficiency of exploration in model-based deep reinforcement learning, and on meta-learning suitable probabilistic models from related tasks.

Andreas Krause is a Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. He also serves as Academic Co-Director of the Swiss Data Science Center and Chair of the ETH AI Center, and co-founded the ETH spin-off LatticeFlow. Before that he was an Assistant Professor of Computer Science at Caltech. He received his Ph.D. in Computer Science from Carnegie Mellon University (2008) and his Diplom in Computer Science and Mathematics from the Technical University of Munich, Germany (2004). He is a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received ERC Starting Investigator and ERC Consolidator grants, the Rössler Prize, the German Pattern Recognition Award, an NSF CAREER award, the Okawa Foundation Research Grant as well as the ETH Golden Owl teaching award. His research has received awards at several premier conferences and journals, including the ACM SIGKDD Test of Time award 2019 and the ICML Test of Time award 2020. Andreas Krause served as Program Co-Chair for ICML 2018 and is serving as Action Editor for the Journal of Machine Learning Research.

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