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Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Jun 23, 2020
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu

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Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

Apr 27, 2020
Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, Shixiang Gu

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A Divergence Minimization Perspective on Imitation Learning Methods

Nov 06, 2019
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, Shixiang Gu

* Published at Conference on Robot Learning (CoRL) 2019. For datasets and reproducing results please refer to 

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Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?

Sep 23, 2019
Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, Sergey Levine

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Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

Aug 13, 2019
Ofir Nachum, Michael Ahn, Hugo Ponte, Shixiang Gu, Vikash Kumar

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Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

Jul 08, 2019
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard

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Dynamics-Aware Unsupervised Discovery of Skills

Jul 02, 2019
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, Karol Hausman

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Language as an Abstraction for Hierarchical Deep Reinforcement Learning

Jun 18, 2019
Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn

* 20 pages, 21 figures 

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Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

Oct 09, 2018
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

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Data-Efficient Hierarchical Reinforcement Learning

Oct 05, 2018
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

* NIPS 2018 

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Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

Oct 02, 2018
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

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The Mirage of Action-Dependent Baselines in Reinforcement Learning

Apr 06, 2018
George Tucker, Surya Bhupatiraju, Shixiang Gu, Richard E. Turner, Zoubin Ghahramani, Sergey Levine

* Updated to address comments from ICLR workshop reviewers 

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Temporal Difference Models: Model-Free Deep RL for Model-Based Control

Feb 25, 2018
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, Sergey Levine

* To appear in ICLR 2018 

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Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

Nov 18, 2017
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine

* Videos of our experiments are available at: 

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Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

Oct 16, 2017
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck

* Add supplementary material 

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Categorical Reparameterization with Gumbel-Softmax

Aug 05, 2017
Eric Jang, Shixiang Gu, Ben Poole

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Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

Jun 01, 2017
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Bernhard Schölkopf, Sergey Levine

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Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

Feb 27, 2017
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine

* Conference Paper at the International Conference on Learning Representations (ICLR) 2017 

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Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates

Nov 23, 2016
Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine

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Continuous Deep Q-Learning with Model-based Acceleration

Mar 02, 2016
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine

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MuProp: Unbiased Backpropagation for Stochastic Neural Networks

Feb 25, 2016
Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih

* Published as a conference paper at ICLR 2016 

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Neural Adaptive Sequential Monte Carlo

Nov 16, 2015
Shixiang Gu, Zoubin Ghahramani, Richard E. Turner

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Towards Deep Neural Network Architectures Robust to Adversarial Examples

Apr 09, 2015
Shixiang Gu, Luca Rigazio

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