Natasha Jaques
Natasha Jaques
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Less is More: Generating Grounded Navigation Instructions from Landmarks
We study the automatic generation of natural language navigation instructions in visually realistic indoor environments. Existing generators suffer from poor visual grounding, skip steps, and hallucinate objects. We address this using a large language model which incorporates visual landmark detection.. The model dramatically increases the quality of generated instructions, such that humans can follow them with a 71% success rate (SR); just shy of the 75% SR of real human instructions.
S. Wang
,
C. Montgomery
,
J. Orbay
,
V. Birodkar
,
A. Faust
,
I. Gur
,
Natasha Jaques
,
A. Waters
,
J. Baldridge
,
P. Anderson
2022
In
Computer Vision and Pattern Recognition (CVPR)
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Dataset
Environment Generation for Zero-Shot Compositional Reinforcement Learning
We analyze and improve upon PAIRED in the case of learning to generate challenging compositional tasks. We apply our improved algorithm to the complex task of training RL agents to navigate websites, and find that it is able to generating a challenging curriculum of novel sites. We achieve a 4x improvement over the strongest web navigation baselines, and deploy our model to navigate real-world websites..
I. Gur
,
Natasha Jaques
,
K. Malta
,
M. Tiwari
,
H. Lee
,
A. Faust
2021
In
Neural Information Processing Systems (NeurIPS)
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Code
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
PsiPhi-Learning learns successor representations for the policies of other agents and the ego agent, using a shared underlying state representation. Learning from other agents helps the agent take better actions at inference time, and learning from RL experience improves modeling of other agents.
A. Filos
,
C. Lyle
,
Y. Gal
,
S. Levine
,
Natasha Jaques
*
,
G. Farquhar
*
2021
In
International Conference on Machine Learning (ICML)
Oral (top 3% of submissions)
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Project
ICML talk
Emergent Social Learning via Multi-agent Reinforcement Learning
Model-free RL agents fail to learn from experts present in multi-agent environments. By adding a model-based auxiliary loss, we induce social learning, which allows agents to learn how to learn from experts. When deployed to novel environments with new experts, they use social learning to determine how to solve the task, and generalize better than agents trained alone with RL or imitation learning.
Kamal Ndousse
,
Douglas Eck
,
Sergey Levine
,
Natasha Jaques
2021
In
International Conference on Machine Learning (ICML);
NeurIPS Cooperative AI Workshop
Best Paper
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Code
Poster
Slides
Cooperative AI talk
ICML talk
Explore and Control with Adversarial Surprise
Adversarial Surprise creates a competitive game between an Expore policy and a Control policy, which fight to maximize and minimize the amount of entropy an RL agent experiences. We show both theoretically and empirically that this technique fully explores the state space of partially-observed, stochastic environments.
A. Fickinger
*
,
Natasha Jaques
*
,
S. Parajuli
,
M. Chang
,
N. Rhinehart
,
G. Berseth
,
S. Russell
,
S. Levine
2021
In
ICML Unsupervised Reinforcement Learning workshop
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Code
Project
Joint Attention for Multi-Agent Coordination and Social Learning
Joint attention is a critical component of human social cognition. In this paper, we ask whether a mechanism based on shared visual attention can be useful for improving multi-agent coordination and social learning.
D. Lee
,
Natasha Jaques
,
J. Kew
,
D. Eck
,
D. Schuurmans
,
A. Faust
2021
In
ICRA Social Intelligence Workshop
Spotlight talk
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Code
Poster
Video
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
PAIRED trains an agent to generate environments that maximize regret between a pair of learning agents. This creates feasible yet challenging environments, which exploit weaknesses in the agents to make them more robust. PAIRED significantly improves generalization to novel tasks.
Michael Dennis
*
,
Natasha Jaques
*
,
Eugene Vinitsky
,
Alexandre Bayen
,
Stuart Russell
,
Andrew Critch
,
Sergey Levine
2020
In
Neural Information Processing Systems (NeurIPS)
Oral (top 1% of submissions)
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Code
Poster
Slides
Videos
NeurIPS Oral
Science article
Google AI Blog
Human-Centric Dialog Training via Offline Reinforcement Learning
We train dialog models with interactive data from conversations with real humans, using a novel Offline RL technique based on KL-control. Rather than rely on manual ratings, we learn from implicit signals like sentiment, and show that this results in better performance.
Natasha Jaques
*
,
J. H. Shen
*
,
A. Ghandeharioun
,
C. Ferguson
,
A. Lapedriza
,
N. Jones
,
S. Gu
,
R. Picard
2020
In
Empirical Methods in Natural Language Processing (EMNLP)
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Code
Dataset
Slides
EMNLP Talk
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
Existing metrics for automatically evaluating dialog models correlate poorly with human judgements, and are evaluated on static conversation snippets. Instead, we deploy bots to interact live with humans, then approximate human ratings with state-of-the-art accuracy using conversations generated with self-play.
A. Ghandeharioun
*
,
J. H. Shen
*
,
Natasha Jaques
*
,
C. Ferguson
,
N. Jones
,
A. Lapedriza
,
R. Picard
2019
In
Neural Information Processing Systems (NeurIPS)
PDF
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Code
Dataset
Poster
Automatic Triage and Analysis of Online Suicide Risk with Document Embeddings and Latent Dirichlet Allocation
To predict which users are at risk of suicide based on a small dataset of online posts, we leverage pre-trained sentence embeddings from large language models, and achieve high F1 scores (.83-.92). We further analyze users’ posts to determine which topics are most associated with suicidal users.
N. Jones
,
Natasha Jaques
,
P. Pataranutaporn
,
A. Ghandeharioun
,
R. Picard
2019
In
Affective Computing and Intelligence Interaction (ACII) workshop on Machine Learning for Mental Health
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