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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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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. ∙
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