Natasha Jaques

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Google ∙ MIT

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

A wide range of reinforcement learning (RL) problems - including robustn...
12/03/2020 ∙ by Michael Dennis, et al. ∙ 0 ∙ share

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Human-centric Dialog Training via Offline Reinforcement Learning

How can we train a dialog model to produce better conversations by learn...
10/12/2020 ∙ by Natasha Jaques, et al. ∙ 4 ∙ share

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Learning Social Learning

Social learning is a key component of human and animal intelligence. By ...
10/01/2020 ∙ by Kamal Ndousse, et al. ∙ 35 ∙ share

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Hierarchical Reinforcement Learning for Open-Domain Dialog

Open-domain dialog generation is a challenging problem; maximum likeliho...
09/17/2019 ∙ by Abdelrhman Saleh, et al. ∙ 5 ∙ share

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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...
06/30/2019 ∙ by Natasha Jaques, et al. ∙ 9 ∙ share

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Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

Building an open-domain conversational agent is a challenging problem. C...
06/21/2019 ∙ by Asma Ghandeharioun, et al. ∙ 3 ∙ share

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Tackling Climate Change with Machine Learning

Climate change is one of the greatest challenges facing humanity, and we...
06/10/2019 ∙ by David Rolnick, et al. ∙ 1 ∙ share

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Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

We propose a unified mechanism for achieving coordination and communicat...
10/19/2018 ∙ by Natasha Jaques, et al. ∙ 0 ∙ share

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Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

We derive a new intrinsic social motivation for multi-agent reinforcemen...
10/19/2018 ∙ by Natasha Jaques, et al. ∙ 0 ∙ share

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Learning via social awareness: improving sketch representations with facial feedback

In the quest towards general artificial intelligence (AI), researchers h...
02/13/2018 ∙ by Natasha Jaques, et al. ∙ 0 ∙ share

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Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

This paper proposes a general method for improving the structure and qua...
11/09/2016 ∙ by Natasha Jaques, et al. ∙ 0 ∙ share

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Natasha Jaques

Featured Co-authors

Yoshua Bengio

Sergey Levine

Nando de Freitas

José Miguel Hernández-Lobato

Richard E. Turner

Stuart Russell

Joel Z. Leibo

Caglar Gulcehre

Andrew Y. Ng

Pedro A. Ortega

Angeliki Lazaridou

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

Human-centric Dialog Training via Offline Reinforcement Learning

Learning Social Learning

Hierarchical Reinforcement Learning for Open-Domain Dialog

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

Tackling Climate Change with Machine Learning

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

Learning via social awareness: improving sketch representations with facial feedback

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control