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Sergey Levine. UC Berkeley, Physical Intelligence. Verified email at eecs.berkeley.edu - Homepage. Machine Learning Robotics Reinforcement Learning.
I am an Associate professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. In my research, I focus on algorithms that can enable autonomous agents to acquire complex behaviors through learning, especially general-purpose methods that could enable any autonomous system to learn to solve any task.
Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016.
Aug 23, 2016 · Sergey Levine. He teaches robots to watch and learn from their own successes. While serving a nine-month stint at Google, Sergey Levine watched as the company’s AlphaGo program defeated the...
Dec 20, 2021 · Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine. Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When does this hold true, and which algorithmic components are necessary?
Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016.
Sergey Levine (Member, IEEE) received the B.S., M.S., and Ph.D. degrees in computer science from Stanford University, Stanford, CA, USA, in 2009 and 2014, respectively.
Off-Policy Reinforcement Learning for Learning from Logged Data. Uncertainty-Aware Meta-Learning and Safe Adaptation. Learning Human-Like Decision-making Behavior based on Adversarial Inverse Reinforcement Learning. Imitative Models: Learning Flexible Driving Models from Human Data.
Sergey Levine. Associate Professor, UC Berkeley. Program Visits. Summer Cluster: AI, Psychology, and Neuroscience, Summer 2024, Visiting Scientist. Theory of Reinforcement Learning, Fall 2020, Visiting Scientist. Foundations of Deep Learning, Summer 2019.
May 4, 2020 · Sergey Levine, Aviral Kumar, George Tucker, Justin Fu. In this tutorial article, we aim to provide the reader with the conceptual tools needed to get started on research on offline reinforcement learning algorithms: reinforcement learning algorithms that utilize previously collected data, without additional online data collection.
