research
∙
05/22/2023
Training Diffusion Models with Reinforcement Learning
Diffusion models are a class of flexible generative models trained with ...
share
research
∙
04/20/2023
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
Effective offline RL methods require properly handling out-of-distributi...
research
∙
04/20/2023
Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Deep reinforcement learning algorithms that learn policies by trial-and-...
research
∙
04/19/2023
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
We present a system that enables an autonomous small-scale RC car to dri...
research
∙
12/16/2022
Offline Reinforcement Learning for Visual Navigation
Reinforcement learning can enable robots to navigate to distant goals wh...
research
∙
08/16/2022
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning
Deep reinforcement learning is a promising approach to learning policies...
research
∙
01/11/2022
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Recent multi-task learning research argues against unitary scalarization...
research
∙
12/20/2021
RvS: What is Essential for Offline RL via Supervised Learning?
Recent work has shown that supervised learning alone, without temporal d...
research
∙
11/29/2021
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
Reinforcement learning (RL) agents are widely used for solving complex s...
research
∙
10/12/2021
Offline Reinforcement Learning with Implicit Q-Learning
Offline reinforcement learning requires reconciling two conflicting aims...
research
∙
03/14/2021
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
Many modern approaches to offline Reinforcement Learning (RL) utilize be...
research
∙
07/27/2020
Statistical Bootstrapping for Uncertainty Estimation in Off-Policy Evaluation
In reinforcement learning, it is typical to use the empirically observed...
research
∙
06/23/2020
Automatic Data Augmentation for Generalization in Deep Reinforcement Learning
Deep reinforcement learning (RL) agents often fail to generalize to unse...
research
∙
04/28/2020
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
We propose a simple data augmentation technique that can be applied to s...
research
∙
12/10/2019
Imitation Learning via Off-Policy Distribution Matching
When performing imitation learning from expert demonstrations, distribut...
research
∙
12/04/2019
AlgaeDICE: Policy Gradient from Arbitrary Experience
In many real-world applications of reinforcement learning (RL), interact...
research
∙
10/02/2019
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images
Training an agent to solve control tasks directly from high-dimensional ...
research
∙
09/09/2018
Addressing Sample Inefficiency and Reward Bias in Inverse Reinforcement Learning
The Generative Adversarial Imitation Learning (GAIL) framework from Ho &...
research
∙
05/30/2017
Surface Networks
We study data-driven representations for three-dimensional triangle mesh...
research
∙
02/17/2016