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This paper introduces the concept of Language-Guided World Models (LWMs) -- probabilistic models that can simulate environments by reading texts.
Khanh Nguyen and Hal Daumé III. 2019 · 1909
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. 2019 · 1912
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Learning how the world works: Specifications for predictive networks in robots and brains
Paul J Werbos. 1987 · 1987
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An on-line algorithm for dynamic reinforcement learning and planning in reactive environments
Jürgen Schmidhuber. 1990b · 1990
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A possibility for implementing curiosity and boredom in model-building neural controllers
Jürgen Schmidhuber. 1991 · 1991
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Emergent communication with world models
Alexander I Cowen-Rivers and Jason Naradowsky. 2020 · 2002
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. 2020 · 2010
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Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen. 2011 · 2011
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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 to win by reading manuals in a monte-carlo framework
SRK Branavan. 2012 · 2012
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Jürgen Schmidhuber. 2015 · 2015
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Natural language communication with robots
Yonatan Bisk, Deniz Yuret, and Daniel Marcu. 2016 · 2016
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz 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 Reid, Stephen Gould, and Anton Van Den Hengel. 2018 · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine. 2018 · 2018
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David Ha and Jürgen Schmidhuber. 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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Grounding language for transfer in deep reinforcement learning
Karthik Narasimhan, Regina Barzilay, and Tommi Jaakkola. 2018 · 2018
Faith and fate: Limits of transformers on compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jian, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D Hwang, et al. 2023 · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. 2023 · 2023
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Transformers are sample-efficient world models
Vincent Micheli, Eloi Alonso, and François Fleuret. 2023 · 2023
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Langwm: Language grounded world model
Rudra PK Poudel, Harit Pandya, Chao Zhang, and Roberto Cipolla. 2023 · 2023
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Transformer-based world models are happy with 100k interactions
Jan Robine, Marc Höftmann, Tobias Uelwer, and Stefan Harmeling. 2023 · 2023
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Cited alongside, same era.
Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, et al. 2020 · 2020
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Rtfm: Generalising to new environment dynamics via reading
Victor Zhong, Tim Rocktäschel, and Edward Grefenstette. 2020 · 2020
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Can transformers jump around right in natural language? assessing performance transfer from scan
Rahma Chaabouni, Roberto Dessì, and Eugene Kharitonov. 2021 · 2021
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Grounding language to entities and dynamics for generalization in reinforcement learning
Austin W Hanjie, Victor Y Zhong, and Karthik Narasimhan. 2021 · 2021
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Inducing transformer’s compositional generalization ability via auxiliary sequence prediction tasks
Yichen Jiang and Mohit Bansal. 2021 · 2021
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Interactive learning from activity description
Khanh X Nguyen, Dipendra Misra, Robert Schapire, Miroslav Dudík, and Patrick Shafto. 2021 · 2021
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Silg: The multi-environment symbolic interactive language grounding benchmark
Victor Zhong, Austin W. Hanjie, Sida I. Wang, Karthik Narasimhan, and Luke Zettlemoyer. 2021 · 2021
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Training language models with language feedback at scale
Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez. 2023 · 2023
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Show or tell? exploring when (and why) teaching with language outperforms demonstration
Theodore R Sumers, Mark K Ho, Robert D Hawkins, and Thomas L Griffiths. 2023 · 2023
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Read and reap the rewards: Learning to play atari with the help of instruction manuals
Yue Wu, Yewen Fan, Paul Pu Liang, Amos Azaria, Yuanzhi Li, and Tom Mitchell. 2023a · 2023
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Progressively efficient learning
Ruijie Zheng, Khanh Nguyen, Hal Daumé III, Furong Huang, and Karthik Narasimhan. 2023 · 2023
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Towards guaranteed safe ai: A framework for ensuring robust and reliable ai systems
David Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, et al. 2024 · 2024
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Learning universal policies via text-guided video generation
Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, Dale Schuurmans, and Pieter Abbeel. 2024 · 2024
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Learning to model the world with language
Jessy Lin, Yuqing Du, Olivia Watkins, Danijar Hafner, Pieter Abbeel, Dan Klein, and Anca Dragan. 2024 · 2024
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Combo: Compositional world models for embodied multi-agent cooperation
Hongxin Zhang, Zeyuan Wang, Qiushi Lyu, Zheyuan Zhang, Sunli Chen, Tianmin Shu, Yilun Du, and Chuang Gan. 2024 · 2024
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Robodreamer: Learning compositional world models for robot imagination
Siyuan Zhou, Yilun Du, Jiaben Chen, Yandong Li, Dit-Yan Yeung, and Chuang Gan. 2024 · 2024
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