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Sergey Levine
Deep Reinforcement Learning
Inverse Reinforcement Learning
End-to-end Deep Neural Network Policies
Autonomous Robotics
Perception And Control Integration
8056 Berkeley Way West
About
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. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more.
Research Performance Summary
1989-2026
Active Research Span
First Recorded Paper
James Harrison
Year:
1989
Citations:
0
Venue:
LedisFlam
Latest Recorded Paper
RoboReward: General-Purpose Vision-Language Reward Models for Robotics
Year:
2026
Citations:
0
Venue:
arXiv preprint arXiv:2601.00675
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Top Coauthors
Levine
Finn
Research Impact by Period
Papers
79
Citations
2255
Avg. Citations / Paper
28.5
H-Index
24
Papers
425
Citations
70032
Avg. Citations / Paper
164.8
H-Index
115
Papers
229
Citations
139768
Avg. Citations / Paper
610.3
H-Index
141
Papers
20
Citations
4753
Avg. Citations / Paper
237.7
H-Index
16
Papers
4
Citations
291
Avg. Citations / Paper
72.8
H-Index
3
Papers
2
Citations
8
Avg. Citations / Paper
4
H-Index
1
Papers
1
Citations
0
Avg. Citations / Paper
0
H-Index
0
Contact and Professional Links
Detected Research Keywords
Offline Reinforcement Learning
Vision Language Action
Meta Reinforcement Learning
Model Reinforcement Learning
Reinforcement Learning Robotic
Language Action Models
Learning Robotic Manipulation
Guided Policy Search
Hierarchical Reinforcement Learning
Machine Learning Methods