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Aurick Zhou

Ph.D

University of California Berkeley

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Amortized Conditional Normalized Maximum Likelihood

Aurick Zhou,Sergey Levine

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Conservative Q-Learning for Offline Reinforcement Learning

Kumar Aviral,Zhou Aurick,Tucker George,Levine Sergey

NeurIPS, (2020)

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Learning to Walk via Deep Reinforcement Learning

Haarnoja Tuomas,Ha Sehoon,Zhou Aurick,Tan Jie,Tucker George,Levine Sergey

Robotics - Science and Systems, (2019)

Cited by52EIBibtex

Efficient off policy meta reinforcement learning via probabilistic context variables

Kate Rakelly,Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen

International conference on machine learning, pp.5331-5340, (2019)

Cited by95Bibtex

Composable Deep Reinforcement Learning for Robotic Manipulation

Tuomas Haarnoja, Vitchyr Pong,Aurick Zhou, Murtaza Dalal,Pieter Abbeel,Sergey Levine

ICRA, (2018): 6244-6251

Cited by71EIBibtex

Soft Actor-Critic Algorithms and Applications

Tuomas Haarnoja,Aurick Zhou, Kristian Hartikainen,George Tucker,Sehoon Ha,Jie Tan,Vikash Kumar, Henry Zhu,Abhishek Gupta,Pieter Abbeel,Sergey Levine

arXiv: Learning, (2018)

Cited by170EIBibtex

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Tuomas Haarnoja,Aurick Zhou,Pieter Abbeel,Sergey Levine

ICML, (2018): 1856-1865

Cited by684EIBibtex

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, pp.1856-1865, (2018)

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