2023 - Research.com Computer Science in United States Leader Award
2019 - Fellow of Alfred P. Sloan Foundation
His Ecology research incorporates Habitat, Zoology and Identification (biology). He connects Habitat with Ecology in his study. His study on Zoology is mostly dedicated to connecting different topics, such as Scarabaeidae. In his papers, he integrates diverse fields, such as Larva and Scarabaeidae. By researching both Endocrinology and Hormone, Sergey Levine produces research that crosses academic boundaries. His study ties his expertise on Corticosterone together with the subject of Hormone. His Corticosterone study frequently involves adjacent topics like Endocrinology. Internal medicine is frequently linked to Avoidance learning in his study. His Avoidance learning study frequently draws connections between adjacent fields such as Internal medicine.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn;Pieter Abbeel;Sergey Levine.
international conference on machine learning (2017)
Trust Region Policy Optimization
John Schulman;Sergey Levine;Pieter Abbeel;Michael Jordan.
international conference on machine learning (2015)
Trust Region Policy Optimization
John Schulman;Sergey Levine;Philipp Moritz;Michael I. Jordan.
arXiv: Learning (2015)
End-to-end training of deep visuomotor policies
Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel.
Journal of Machine Learning Research (2016)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja;Aurick Zhou;Pieter Abbeel;Sergey Levine.
international conference on machine learning (2018)
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Sergey Levine;Peter Pastor;Alex Krizhevsky;Julian Ibarz.
The International Journal of Robotics Research (2018)
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu;Ethan Holly;Timothy Lillicrap;Sergey Levine.
international conference on robotics and automation (2017)
High-Dimensional Continuous Control Using Generalized Advantage Estimation
John Schulman;Philipp Moritz;Sergey Levine;Michael Jordan.
arXiv: Learning (2015)
Guided Policy Search
Sergey Levine;Vladlen Koltun.
international conference on machine learning (2013)
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov;Alex Irpan;Peter Pastor;Julian Ibarz.
Conference on Robot Learning (2018)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
University of California, Berkeley
Stanford University
University of California, Berkeley
University of California, Berkeley
University of Montreal
University of California, Berkeley
University College London
Intel (United States)
University of Illinois at Urbana-Champaign
University of California, Berkeley
Washington State University
Rutgers, The State University of New Jersey
University of Queensland
National Institute of Advanced Industrial Science and Technology
Anhui University
Chinese University of Hong Kong
ETH Zurich
Brown University
University of Oregon
Agriculture and Agriculture-Food Canada
J. Craig Venter Institute
Freie Universität Berlin
University of Mannheim
University of Brighton
McGill University
Princeton University