Aurick Zhou | DeepAI

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∙ 07/12/2022

Wayformer: Motion Forecasting via Simple Efficient Attention Networks

Motion forecasting for autonomous driving is a challenging task because ...

0 Nigamaa Nayakanti, et al. ∙

∙ 09/27/2021

Training on Test Data with Bayesian Adaptation for Covariate Shift

When faced with distribution shift at test time, deep neural networks of...

0 Aurick Zhou, et al. ∙

∙ 07/15/2021

MURAL: Meta-Learning Uncertainty-Aware Rewards for Outcome-Driven Reinforcement Learning

Exploration in reinforcement learning is a challenging problem: in the w...

12 Kevin Li, et al. ∙

∙ 11/05/2020

Amortized Conditional Normalized Maximum Likelihood

While deep neural networks provide good performance for a range of chall...

0 Aurick Zhou, et al. ∙

∙ 06/08/2020

Conservative Q-Learning for Offline Reinforcement Learning

Effectively leveraging large, previously collected datasets in reinforce...

0 Aviral Kumar, et al. ∙

∙ 03/19/2019

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Deep reinforcement learning algorithms require large amounts of experien...

32 Kate Rakelly, et al. ∙

∙ 12/26/2018

Learning to Walk via Deep Reinforcement Learning

Deep reinforcement learning suggests the promise of fully automated lear...

30 Tuomas Haarnoja, et al. ∙

∙ 12/13/2018

Soft Actor-Critic Algorithms and Applications

Model-free deep reinforcement learning (RL) algorithms have been success...

12 Tuomas Haarnoja, et al. ∙

∙ 03/19/2018

Composable Deep Reinforcement Learning for Robotic Manipulation

Model-free deep reinforcement learning has been shown to exhibit good pe...

0 Tuomas Haarnoja, et al. ∙

∙ 01/04/2018

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

Model-free deep reinforcement learning (RL) algorithms have been demonst...

0 Tuomas Haarnoja, et al. ∙