Denis Yarats | DeepAI

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∙ 06/30/2022

Watch and Match: Supercharging Imitation with Regularized Optimal Transport

Imitation learning holds tremendous promise in learning policies efficie...

7 Siddhant Haldar, et al. ∙

∙ 02/01/2022

CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery

We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsup...

1 Michael (Misha) Laskin, et al. ∙

∙ 01/31/2022

Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning

Recent progress in deep learning has relied on access to large and diver...

10 Denis Yarats, et al. ∙

∙ 10/28/2021

URLB: Unsupervised Reinforcement Learning Benchmark

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to s...

15 Michael (Misha) Laskin, et al. ∙

∙ 07/20/2021

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

We present DrQ-v2, a model-free reinforcement learning (RL) algorithm fo...

15 Denis Yarats, et al. ∙

∙ 02/22/2021

Reinforcement Learning with Prototypical Representations

Learning effective representations in image-based environments is crucia...

21 Denis Yarats, et al. ∙

∙ 11/24/2020

Learning Navigation Skills for Legged Robots with Learned Robot Embeddings

Navigation policies are commonly learned on idealized cylinder agents in...

0 Joanne Truong, et al. ∙

∙ 08/28/2020

On the model-based stochastic value gradient for continuous reinforcement learning

Model-based reinforcement learning approaches add explicit domain knowle...

6 Brandon Amos, et al. ∙

∙ 06/23/2020

Automatic Data Augmentation for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...

0 Roberta Raileanu, et al. ∙

∙ 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...

10 Ilya Kostrikov, et al. ∙

∙ 10/09/2019

On the adequacy of untuned warmup for adaptive optimization

Adaptive optimization algorithms such as Adam (Kingma Ba, 2014) are ...

0 Jerry Ma, et al. ∙

∙ 10/03/2019

Generalized Inner Loop Meta-Learning

Many (but not all) approaches self-qualifying as "meta-learning" in deep...

12 Edward Grefenstette, et al. ∙

∙ 10/02/2019

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Training an agent to solve control tasks directly from high-dimensional ...

18 Denis Yarats, et al. ∙

∙ 09/27/2019

The Differentiable Cross-Entropy Method

We study the Cross-Entropy Method (CEM) for the non-convex optimization ...

28 Brandon Amos, et al. ∙

∙ 06/03/2019

Hierarchical Decision Making by Generating and Following Natural Language Instructions

We explore using latent natural language instructions as an expressive a...

4 Hengyuan Hu, et al. ∙

∙ 10/16/2018

Quasi-hyperbolic momentum and Adam for deep learning

Momentum-based acceleration of stochastic gradient descent (SGD) is wide...

0 Jerry Ma, et al. ∙

∙ 12/15/2017

Hierarchical Text Generation and Planning for Strategic Dialogue

End-to-end models for strategic dialogue are challenging to train, becau...

0 Denis Yarats, et al. ∙

∙ 06/16/2017

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

Much of human dialogue occurs in semi-cooperative settings, where agents...

0 Mike Lewis, et al. ∙

∙ 05/08/2017

Convolutional Sequence to Sequence Learning

The prevalent approach to sequence to sequence learning maps an input se...

0 Jonas Gehring, et al. ∙

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