
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Deep latent variable models have become a popular model choice due to th...
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A Divergence Minimization Perspective on Imitation Learning Methods
In many settings, it is desirable to learn decisionmaking and control p...
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Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success...
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NearOptimal Representation Learning for Hierarchical Reinforcement Learning
We study the problem of representation learning in goalconditioned hier...
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Way OffPolicy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Most deep reinforcement learning (RL) systems are not able to learn effe...
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Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Solving complex, temporallyextended tasks is a longstanding problem in...
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MultiAgent Manipulation via Locomotion using Hierarchical Sim2Real
Manipulation and locomotion are closely related problems that are often ...
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MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Deep neural networks are powerful parametric models that can be trained ...
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Interpolated Policy Gradient: Merging OnPolicy and OffPolicy Gradient Estimation for Deep Reinforcement Learning
Offpolicy modelfree deep reinforcement learning methods using previous...
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Sequence Tutor: Conservative FineTuning of Sequence Generation Models with KLcontrol
This paper proposes a general method for improving the structure and qua...
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Deep Reinforcement Learning for Robotic Manipulation with Asynchronous OffPolicy Updates
Reinforcement learning holds the promise of enabling autonomous robots t...
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Continuous Deep QLearning with Modelbased Acceleration
Modelfree reinforcement learning has been successfully applied to a ran...
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Categorical Reparameterization with GumbelSoftmax
Categorical variables are a natural choice for representing discrete str...
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Neural Adaptive Sequential Monte Carlo
Sequential Monte Carlo (SMC), or particle filtering, is a popular class ...
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Towards Deep Neural Network Architectures Robust to Adversarial Examples
Recent work has shown deep neural networks (DNNs) to be highly susceptib...
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Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Deep reinforcement learning algorithms can learn complex behavioral skil...
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Temporal Difference Models: ModelFree Deep RL for ModelBased Control
Modelfree reinforcement learning (RL) is a powerful, general tool for l...
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The Mirage of ActionDependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of modelfree reinforcem...
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DynamicsAware Unsupervised Discovery of Skills
Conventionally, modelbased reinforcement learning (MBRL) aims to learn ...
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Shixiang Gu
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Research Intern at Google, Ph.D. candidate and Research Assistant at University of Cambridge