Offline Reinforcement Learning Professors in Global

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Kevin Luck
Vrije Universiteit Amsterdam Netherlands
reinforcement learning
deep reinforcement learning
robot learning
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Adam Wierman
California Institute of Technology United States of America
online learning
online algorithms
online optimization
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Adam Wierman
California Institute of Technology United States of America
online learning
online algorithms
online optimization
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Adam Wierman
California Institute of Technology United States of America
online learning
online algorithms
online optimization
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Qingyun Wu
Pennsylvania State University United States of America
reinforcement learning
online optimization
learning
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Rahul Jain
University of Southern California United States of America
reinforcement learning
online learning
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Alexander Rakhlin
Massachusetts Institute of Technology United States of America
reinforcement learning
online learning
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Ciara Pike-burke
Imperial College London United Kingdom
reinforcement learning
online learning
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Sadegh Talebi
University of Copenhagen Denmark
reinforcement learning
online learning
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Haipeng Luo
University of Southern California United States of America
reinforcement learning
online learning
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Rahul Jain
University of Southern California United States of America
reinforcement learning
online learning
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Haipeng Luo
University of Southern California United States of America
reinforcement learning
online learning
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Marc Gendron-bellemare
University of Montreal Canada
reinforcement learning
online learning
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Colin Bellinger
University of Ottawa Canada
reinforcement learning
safe reinforcement learning
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Rahul Jain
University of Southern California United States of America
online learning
statistical reinforcement learning
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Matthias Bickel
ETH Zurich Switzerland
reinforcement learning
inverse reinforcement learning
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Florian Shkurti
University of Toronto Canada
reinforcement learning
inverse reinforcement learning
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Eraldine Williams-shakespeare
University of Technology, Jamaica Jamaica
online learning
distance learning
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Yuhua Zhu
University of California Los Angeles United States of America
reinforcement learning
continuous-time reinforcement learning
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Silun Zhang
KTH Royal Institute of Technology Sweden
safe reinforcement learning
risk-averse reinforcement learning
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Wendelin Böhmer
Delft University of Technology Netherlands
reinforcement learning
multi-agent reinforcement learning
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