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Mastering Atari with Discrete World Models

Oct 05, 2020
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba

* 8 pages, 4 figures, 4 tables 

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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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Physically Embedded Planning Problems: New Challenges for Reinforcement Learning

Sep 11, 2020
Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Peter Karkus, Sébastien Racanière, Lars Buesing, Timothy Lillicrap, Nicolas Heess


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dm_control: Software and Tasks for Continuous Control

Jun 22, 2020
Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, Nicolas Heess

* arXiv admin note: text overlap with arXiv:1801.00690 

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Dream to Control: Learning Behaviors by Latent Imagination

Dec 03, 2019
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi

* 9 pages, 12 figures 

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LOGAN: Latent Optimisation for Generative Adversarial Networks

Dec 02, 2019
Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, Timothy Lillicrap


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Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

Nov 19, 2019
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver


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Deep Learning without Weight Transport

May 31, 2019
Mohamed Akrout, Collin Wilson, Peter C. Humphreys, Timothy Lillicrap, Douglas Tweed


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Deep Compressed Sensing

May 18, 2019
Yan Wu, Mihaela Rosca, Timothy Lillicrap

* ICML 2019 

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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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Episodic Curiosity through Reachability

Feb 22, 2019
Nikolay Savinov, Anton Raichuk, Rapha√ęl Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly

* Accepted to ICLR 2019. Code at https://github.com/google-research/episodic-curiosity/. Videos at https://sites.google.com/view/episodic-curiosity/ 

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Learning to Make Analogies by Contrasting Abstract Relational Structure

Jan 31, 2019
Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy Lillicrap


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An investigation of model-free planning

Jan 11, 2019
Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap


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Learning Latent Dynamics for Planning from Pixels

Dec 03, 2018
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson

* 10 pages, 5 figures, 1 table 

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Learning Attractor Dynamics for Generative Memory

Nov 23, 2018
Yan Wu, Greg Wayne, Karol Gregor, Timothy Lillicrap


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Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

Oct 31, 2018
Danijar Hafner, Dustin Tran, Timothy Lillicrap, Alex Irpan, James Davidson

* 9 pages, 5 figures 

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Optimizing Agent Behavior over Long Time Scales by Transporting Value

Oct 15, 2018
Chia-Chun Hung, Timothy Lillicrap, Josh Abramson, Yan Wu, Mehdi Mirza, Federico Carnevale, Arun Ahuja, Greg Wayne


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Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures

Jul 12, 2018
Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy Lillicrap


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Measuring abstract reasoning in neural networks

Jul 11, 2018
David G. T. Barrett, Felix Hill, Adam Santoro, Ari S. Morcos, Timothy Lillicrap

* ICML 2018 

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Relational recurrent neural networks

Jun 28, 2018
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, Theophane Weber, Daan Wierstra, Oriol Vinyals, Razvan Pascanu, Timothy Lillicrap


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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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The Kanerva Machine: A Generative Distributed Memory

Jun 18, 2018
Yan Wu, Greg Wayne, Alex Graves, Timothy Lillicrap

* Published as a conference paper at ICLR 2018 (corrected typos in revision) 

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Distributed Distributional Deterministic Policy Gradients

Apr 23, 2018
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap


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Recall Traces: Backtracking Models for Efficient Reinforcement Learning

Apr 02, 2018
Anirudh Goyal, Philemon Brakel, William Fedus, Timothy Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio

* In Review at ICML 2018 

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Unsupervised Predictive Memory in a Goal-Directed Agent

Mar 28, 2018
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matt Botvinick, Demis Hassabis, Timothy Lillicrap


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DeepMind Control Suite

Jan 02, 2018
Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy Lillicrap, Martin Riedmiller

* 24 pages, 7 figures, 2 tables 

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Matching Networks for One Shot Learning

Dec 29, 2017
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra


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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Dec 05, 2017
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis


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StarCraft II: A New Challenge for Reinforcement Learning

Aug 16, 2017
Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich K√ľttler, John Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing

* Collaboration between DeepMind & Blizzard. 20 pages, 9 figures, 2 tables 

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