Shixiang Gu | DeepAI

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∙ 06/05/2020

Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Most reinforcement learning (RL) algorithms assume online access to the ...

10 Tatsuya Matsushima, et al. ∙

∙ 04/27/2020

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

Reinforcement learning provides a general framework for learning robotic...

0 Archit Sharma, et al. ∙

∙ 11/06/2019

A Divergence Minimization Perspective on Imitation Learning Methods

In many settings, it is desirable to learn decision-making and control p...

15 Seyed Kamyar Seyed Ghasemipour, et al. ∙

∙ 09/23/2019

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

Hierarchical reinforcement learning has demonstrated significant success...

14 Ofir Nachum, et al. ∙

∙ 08/13/2019

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

Manipulation and locomotion are closely related problems that are often ...

7 Ofir Nachum, et al. ∙

∙ 07/02/2019

Dynamics-Aware Unsupervised Discovery of Skills

Conventionally, model-based reinforcement learning (MBRL) aims to learn ...

0 Archit Sharma, et al. ∙

∙ 06/30/2019

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

Most deep reinforcement learning (RL) systems are not able to learn effe...

9 Natasha Jaques, et al. ∙

∙ 06/18/2019

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

Solving complex, temporally-extended tasks is a long-standing problem in...

9 Yiding Jiang, et al. ∙

∙ 10/09/2018

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives

Deep latent variable models have become a popular model choice due to th...

18 George Tucker, et al. ∙

∙ 10/02/2018

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

We study the problem of representation learning in goal-conditioned hier...

12 Ofir Nachum, et al. ∙

∙ 02/27/2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning

Policy gradient methods are a widely used class of model-free reinforcem...

0 George Tucker, et al. ∙

∙ 02/25/2018

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

Model-free reinforcement learning (RL) is a powerful, general tool for l...

0 Vitchyr Pong, et al. ∙

∙ 11/18/2017

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

Deep reinforcement learning algorithms can learn complex behavioral skil...

0 Benjamin Eysenbach, et al. ∙

∙ 06/01/2017

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

Off-policy model-free deep reinforcement learning methods using previous...

0 Shixiang Gu, et al. ∙

∙ 11/09/2016

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

This paper proposes a general method for improving the structure and qua...

0 Natasha Jaques, et al. ∙

∙ 11/03/2016

Categorical Reparameterization with Gumbel-Softmax

Categorical variables are a natural choice for representing discrete str...

0 Eric Jang, et al. ∙

∙ 10/03/2016

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

Reinforcement learning holds the promise of enabling autonomous robots t...

0 Shixiang Gu, et al. ∙

∙ 03/02/2016

Continuous Deep Q-Learning with Model-based Acceleration

Model-free reinforcement learning has been successfully applied to a ran...

0 Shixiang Gu, et al. ∙

∙ 11/16/2015

MuProp: Unbiased Backpropagation for Stochastic Neural Networks

Deep neural networks are powerful parametric models that can be trained ...

0 Shixiang Gu, et al. ∙

∙ 06/10/2015

Neural Adaptive Sequential Monte Carlo

Sequential Monte Carlo (SMC), or particle filtering, is a popular class ...

0 Shixiang Gu, et al. ∙

∙ 12/11/2014

Towards Deep Neural Network Architectures Robust to Adversarial Examples

Recent work has shown deep neural networks (DNNs) to be highly susceptib...

0 Shixiang Gu, et al. ∙