Aurick Zhou

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 21

Top Topics

Reinforcement Learning

Temporal Difference Learning

State Action Space

Covariate Shift

Top Venues

Publications

Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks. ICRA (2023)
Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp
MotionLM: Multi-Agent Motion Forecasting as Language Modeling. ICCV (2023)
Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp
MotionLM: Multi-Agent Motion Forecasting as Language Modeling. CoRR (2023)
Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks. CoRR (2022)
Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H. Pong, Aurick Zhou, Justin Yu, Sergey Levine
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. ICML (2021)
Aurick Zhou, Sergey Levine
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation. ICML (2021)
Aurick Zhou, Sergey Levine
Bayesian Adaptation for Covariate Shift. NeurIPS (2021)
Aurick Zhou, Sergey Levine
Training on Test Data with Bayesian Adaptation for Covariate Shift. CoRR (2021)
Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning. CoRR (2021)
Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS (2020)
Aurick Zhou, Sergey Levine
Amortized Conditional Normalized Maximum Likelihood. CoRR (2020)
Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine
Conservative Q-Learning for Offline Reinforcement Learning. CoRR (2020)
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine
Learning to Walk Via Deep Reinforcement Learning. Robotics: Science and Systems (2019)
Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. ICML (2019)
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. CoRR (2019)
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine
Composable Deep Reinforcement Learning for Robotic Manipulation. ICRA (2018)
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. ICML (2018)
Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan, George Tucker, Sergey Levine
Learning to Walk via Deep Reinforcement Learning. CoRR (2018)
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine
Soft Actor-Critic Algorithms and Applications. CoRR (2018)
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor. CoRR (2018)
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou, Murtaza Dalal, Pieter Abbeel, Sergey Levine
Composable Deep Reinforcement Learning for Robotic Manipulation. CoRR (2018)