research
∙
06/05/2020
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Most reinforcement learning (RL) algorithms assume online access to the ...
share
research
∙
04/27/2020
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Reinforcement learning provides a general framework for learning robotic...
research
∙
11/06/2019
A Divergence Minimization Perspective on Imitation Learning Methods
In many settings, it is desirable to learn decision-making and control p...
research
∙
09/23/2019
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Hierarchical reinforcement learning has demonstrated significant success...
research
∙
08/13/2019
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Manipulation and locomotion are closely related problems that are often ...
research
∙
07/02/2019
Dynamics-Aware Unsupervised Discovery of Skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn ...
research
∙
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...
research
∙
06/18/2019
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Solving complex, temporally-extended tasks is a long-standing problem in...
research
∙
10/09/2018
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
Deep latent variable models have become a popular model choice due to th...
research
∙
10/02/2018
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
We study the problem of representation learning in goal-conditioned hier...
research
∙
02/27/2018
The Mirage of Action-Dependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of model-free reinforcem...
research
∙
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...
research
∙
11/18/2017
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Deep reinforcement learning algorithms can learn complex behavioral skil...
research
∙
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...
research
∙
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...
research
∙
11/03/2016
Categorical Reparameterization with Gumbel-Softmax
Categorical variables are a natural choice for representing discrete str...
research
∙
10/03/2016
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Reinforcement learning holds the promise of enabling autonomous robots t...
research
∙
03/02/2016
Continuous Deep Q-Learning with Model-based Acceleration
Model-free reinforcement learning has been successfully applied to a ran...
research
∙
11/16/2015
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Deep neural networks are powerful parametric models that can be trained ...
research
∙
06/10/2015
Neural Adaptive Sequential Monte Carlo
Sequential Monte Carlo (SMC), or particle filtering, is a popular class ...
research
∙
12/11/2014