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Learning to Induce Causal Structure


Apr 11, 2022
Nan Rosemary Ke, Silvia Chiappa, Jane Wang, Jorg Bornschein, Theophane Weber, Anirudh Goyal, Matthew Botvinic, Michael Mozer, Danilo Jimenez Rezende

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Retrieval-Augmented Reinforcement Learning


Mar 09, 2022
Anirudh Goyal, Abram L. Friesen, Andrea Banino, Theophane Weber, Nan Rosemary Ke, Adria Puigdomenech Badia, Arthur Guez, Mehdi Mirza, Peter C. Humphreys, Ksenia Konyushkova, Michal Valko, Simon Osindero, Timothy Lillicrap, Nicolas Heess, Charles Blundell

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Muesli: Combining Improvements in Policy Optimization


Apr 13, 2021
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt

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Synthetic Returns for Long-Term Credit Assignment


Feb 24, 2021
David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt Botvinick, Hado van Hasselt, Francis Song

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A case for new neural network smoothness constraints


Dec 21, 2020
Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed

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Beyond Tabula-Rasa: a Modular Reinforcement Learning Approach for Physically Embedded 3D Sokoban


Oct 03, 2020
Peter Karkus, Mehdi Mirza, Arthur Guez, Andrew Jaegle, Timothy Lillicrap, Lars Buesing, Nicolas Heess, Theophane Weber

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Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning


Apr 23, 2020
Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber

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Causally Correct Partial Models for Reinforcement Learning


Feb 07, 2020
Danilo J. Rezende, Ivo Danihelka, George Papamakarios, Nan Rosemary Ke, Ray Jiang, Theophane Weber, Karol Gregor, Hamza Merzic, Fabio Viola, Jane Wang, Jovana Mitrovic, Frederic Besse, Ioannis Antonoglou, Lars Buesing

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