538 citations · 781 across the 5 of their papers we have counts for
8 papers
Training on Test Data with Bayesian Adaptation for Covariate Shift
Aurick Zhou, Sergey Levine
When faced with distribution shift at test time, deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. While improving the robustness of neu…
MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning
Kevin Li, Abhishek Gupta, Ashwin Reddy +4
Exploration in reinforcement learning is a challenging problem: in the worst case, the agent must search for high-reward states that could be hidden anywhere in the state space. Ca…
Amortized Conditional Normalized Maximum Likelihood: Reliable Out of Distribution Uncertainty Estimation
Aurick Zhou, Sergey Levine
While deep neural networks provide good performance for a range of challenging tasks, calibration and uncertainty estimation remain major challenges, especially under distribution…
Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar, Aurick Zhou, George Tucker +1
Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise…
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen +2
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable a…
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen +8
Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks. However, these methods…