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Deep Reinforcement Learning and its Neuroscientific Implications

Jul 07, 2020
Matthew Botvinick, Jane X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson

* 22 pages, 5 figures 

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The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget

Apr 24, 2020
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick, Sergey Levine

* Published as a conference paper at ICLR 2020 

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MEMO: A Deep Network for Flexible Combination of Episodic Memories

Jan 29, 2020
Andrea Banino, Adrià Puigdomènech Badia, Raphael Köster, Martin J. Chadwick, Vinicius Zambaldi, Demis Hassabis, Caswell Barry, Matthew Botvinick, Dharshan Kumaran, Charles Blundell

* 9 pages, 2 figures, 3 tables, to be published as a conference paper at ICLR 2020 

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Emergent Systematic Generalization in a Situated Agent

Oct 28, 2019
Felix Hill, Andrew Lampinen, Rosalia Schneider, Stephen Clark, Matthew Botvinick, James L. McClelland, Adam Santoro

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Meta-learning of Sequential Strategies

May 08, 2019
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin Miller, Mohammad Azar, Ian Osband, Neil Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg

* DeepMind Technical Report (15 pages, 6 figures) 

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Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette

Apr 18, 2019
Adam Santoro, Felix Hill, David Barrett, David Raposo, Matthew Botvinick, Timothy Lillicrap

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InfoBot: Transfer and Exploration via the Information Bottleneck

Apr 04, 2019
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio, Sergey Levine

* Accepted at ICLR'19 

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Multi-Object Representation Learning with Iterative Variational Inference

Mar 01, 2019
Klaus Greff, Rapha√ęl Lopez Kaufmann, Rishab Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, Alexander Lerchner

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Causal Reasoning from Meta-reinforcement Learning

Jan 23, 2019
Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, Zeb Kurth-Nelson

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Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

Nov 04, 2018
Jakob N. Foerster, Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew Botvinick, Michael Bowling

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Relational Forward Models for Multi-Agent Learning

Sep 28, 2018
Andrea Tacchetti, H. Francis Song, Pedro A. M. Mediano, Vinicius Zambaldi, Neil C. Rabinowitz, Thore Graepel, Matthew Botvinick, Peter W. Battaglia

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Been There, Done That: Meta-Learning with Episodic Recall

Jul 06, 2018
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick

* ICML 2018 

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Relational Deep Reinforcement Learning

Jun 28, 2018
Vinicius Zambaldi, David Raposo, Adam Santoro, Victor Bapst, Yujia Li, Igor Babuschkin, Karl Tuyls, David Reichert, Timothy Lillicrap, Edward Lockhart, Murray Shanahan, Victoria Langston, Razvan Pascanu, Matthew Botvinick, Oriol Vinyals, Peter Battaglia

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SCAN: Learning Hierarchical Compositional Visual Concepts

Jun 06, 2018
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, Alexander Lerchner

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DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

Jun 06, 2018
Irina Higgins, Arka Pal, Andrei A. Rusu, Loic Matthey, Christopher P Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, Alexander Lerchner

* ICML 2017 

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On the importance of single directions for generalization

May 22, 2018
Ari S. Morcos, David G. T. Barrett, Neil C. Rabinowitz, Matthew Botvinick

* ICLR 2018 conference paper; added additional methodological details 

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Machine Theory of Mind

Mar 12, 2018
Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick

* 21 pages, 15 figures 

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One-shot Learning with Memory-Augmented Neural Networks

May 19, 2016
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap

* 13 pages, 8 figures 

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