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Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration.
Watch, try, learn: Meta-learning from demonstrations and reward
Allan Zhou, Eric Jang, Daniel Kappler, Alexander Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, and Chelsea Finn. 2020 · 1906
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Syntactic Structures
Noam Chomsky. 1957 · 1957
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Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta…-hook
Jurgen Schmidhuber. 1987 · 1987
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Learning to learn
Sebastian Thrun and Lorien Y. Pratt. 1998 · 1998
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On the optimization of a synaptic learning rule
Samy Bengio, Yoshua Bengio, Jocelyn Cloutier, and Jan Gecsei. 2007 · 2007
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. 2011 · 2011
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016 · 2016
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Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, P. Abbeel, and Wojciech Zaremba. 2017 · 2017
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Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian D. Reid, Stephen Gould, and Anton van den Hengel. 2018 · 2018
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Learning with latent language
Jacob Andreas, Dan Klein, and Sergey Levine. 2018 · 2018
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Babyai: A platform to study the sample efficiency of grounded language learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, and Yoshua Bengio. 2018 · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni. 2018 · 2018
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Mapping instructions to actions in 3d environments with visual goal prediction
Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, and Yoav Artzi. 2018 · 2018
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Hierarchical reinforcement learning for zero-shot generalization with subtask dependencies
Sungryull Sohn, Junhyuk Oh, and Honglak Lee. 2018 · 2018
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Language-goal imagination to foster creative exploration in deep rl
Tristan Karch, Nicolas Lair, Cédric Colas, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Ford Dominey, and Pierre-Yves Oudeyer. 2020 · 2020
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Zero-shot imitation learning from demonstrations for legged robot visual navigation
Xinlei Pan, Tingnan Zhang, Brian Ichter, Aleksandra Faust, Jie Tan, and Sehoon Ha. 2020 · 2020
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A benchmark for systematic generalization in grounded language understanding
Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, and Brenden M Lake. 2020 · 2020
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ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. 2020 · 2020
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Learning from task descriptions
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew E. Peters. 2020 · 2020
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Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, P. Abbeel, and Sergey Levine. 2018 · 2018
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Counting to explore and generalize in text-based games
Xingdi Yuan, Marc-Alexandre Côté, Alessandro Sordoni, Romain Laroche, Remi Tachet des Combes, Matthew Hausknecht, and Adam Trischler. 2018 · 2018
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Environmental drivers of systematicity and generalization in a situated agent
Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L McClelland, and Adam Santoro. 2019 · 2019
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Hierarchical decision making by generating and following natural language instructions
Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, and Mike Lewis. 2019 · 2019
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Language as an abstraction for hierarchical deep reinforcement learning
YiDing Jiang, Shixiang Shane Gu, Kevin P Murphy, and Chelsea Finn. 2019 · 2019
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Human few-shot learning of compositional instructions
Brenden Lake, Tal Linzen, and Marco Baroni. 2019 · 2019
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Good-enough compositional data augmentation
Jacob Andreas. 2020 · 2020
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Ask your humans: Using human instructions to improve generalization in reinforcement learning
Valerie Chen, Abhinav Gupta, and Kenneth Marino. 2021 · 2021
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Meta-learning to compositionally generalize
Henry Conklin, Bailin Wang, Kenny Smith, and Ivan Titov. 2021 · 2021
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Multitasking inhibits semantic drift
Athul Paul Jacob, Mike Lewis, and Jacob Andreas. 2021 · 2021
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Word meaning in minds and machines
Brenden Lake and Gregory Murphy. 2021 · 2021
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Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2021 · 2021
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Systematic generalization on gSCAN: What is nearly solved and what is next?
Linlu Qiu, Hexiang Hu, Bowen Zhang, Peter Shaw, and Fei Sha. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2021 · 2021
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Safe reinforcement learning with natural language constraints
Tsung-Yen Yang, Michael Hu, Yinlam Chow, Peter Ramadge, and Karthik R Narasimhan. 2021 · 2021
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