Search Results for author: Denis Yarats

Found 21 papers, 18 papers with code

Watch and Match: Supercharging Imitation with Regularized Optimal Transport

no code implementations30 Jun 2022 Siddhant Haldar, Vaibhav Mathur, Denis Yarats, Lerrel Pinto

Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7. 8X faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods.

Imitation Learning

CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery

1 code implementation1 Feb 2022 Michael Laskin, Hao liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel

We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors.

Contrastive Learning reinforcement-learning +2

URLB: Unsupervised Reinforcement Learning Benchmark

1 code implementation28 Oct 2021 Michael Laskin, Denis Yarats, Hao liu, Kimin Lee, Albert Zhan, Kevin Lu, Catherine Cang, Lerrel Pinto, Pieter Abbeel

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks.

Continuous Control reinforcement-learning +2

Reinforcement Learning with Prototypical Representations

1 code implementation22 Feb 2021 Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto

Unfortunately, in RL, representation learning is confounded with the exploratory experience of the agent -- learning a useful representation requires diverse data, while effective exploration is only possible with coherent representations.

Continuous Control reinforcement-learning +3

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

1 code implementation28 Aug 2020 Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.

Continuous Control Humanoid Control +4

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

4 code implementations ICLR 2021 Ilya Kostrikov, Denis Yarats, Rob Fergus

We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training.

Atari Games 100k Continuous Control +4

Generalized Inner Loop Meta-Learning

3 code implementations3 Oct 2019 Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala

Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.

Meta-Learning reinforcement-learning +1

The Differentiable Cross-Entropy Method

1 code implementation ICML 2020 Brandon Amos, Denis Yarats

We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters.

BIG-bench Machine Learning Continuous Control +1

Hierarchical Decision Making by Generating and Following Natural Language Instructions

1 code implementation NeurIPS 2019 Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, Mike Lewis

We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making.

Decision Making

Quasi-hyperbolic momentum and Adam for deep learning

2 code implementations ICLR 2019 Jerry Ma, Denis Yarats

Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning.

Stochastic Optimization

Hierarchical Text Generation and Planning for Strategic Dialogue

1 code implementation ICML 2018 Denis Yarats, Mike Lewis

End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.

Decision Making reinforcement-learning +2

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

no code implementations EMNLP 2017 Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, Dhruv Batra

Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions.

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

1 code implementation16 Jun 2017 Mike Lewis, Denis Yarats, Yann N. Dauphin, Devi Parikh, Dhruv Batra

Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions.

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