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Game Plan: What AI can do for Football, and What Football can do for AI

Nov 18, 2020
Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adria Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Perolat, Bart De Vylder, Ali Eslami, Mark Rowland, Andrew Jaegle, Remi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis


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Negotiating Team Formation Using Deep Reinforcement Learning

Oct 20, 2020
Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel

* Artificial Intelligence 288 (2020): 103356 

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EigenGame: PCA as a Nash Equilibrium

Oct 01, 2020
Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel


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Learning to Play No-Press Diplomacy with Best Response Policy Iteration

Jun 17, 2020
Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach


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Smooth markets: A basic mechanism for organizing gradient-based learners

Jan 18, 2020
David Balduzzi, Wojciech M Czarnecki, Thomas W Anthony, Ian M Gemp, Edward Hughes, Joel Z Leibo, Georgios Piliouras, Thore Graepel

* ICLR 2020 
* 18 pages, 3 figures 

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Biases for Emergent Communication in Multi-agent Reinforcement Learning

Dec 11, 2019
Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel

* Accepted at NeurIPS 2019 

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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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A Generalized Training Approach for Multiagent Learning

Sep 27, 2019
Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos


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A Neural Architecture for Designing Truthful and Efficient Auctions

Jul 11, 2019
Andrea Tacchetti, DJ Strouse, Marta Garnelo, Thore Graepel, Yoram Bachrach


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Differentiable Game Mechanics

May 13, 2019
Alistair Letcher, David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

* Journal of Machine Learning Research (JMLR), v20 (84) 1-40, 2019 
* JMLR 2019, journal version of arXiv:1802.05642 

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Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research

Mar 11, 2019
Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel

* 16 pages, 2 figures 

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Emergent Coordination Through Competition

Feb 21, 2019
Siqi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel


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Open-ended Learning in Symmetric Zero-sum Games

Jan 23, 2019
David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel

* 18 pages, 7 figures 

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Malthusian Reinforcement Learning

Dec 17, 2018
Joel Z. Leibo, Julien Perolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar Du√©√Īez-Guzm√°n, Peter Sunehag, Iain Dunning, Thore Graepel

* 9 pages, 2 tables, 4 figures 

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Re-evaluating Evaluation

Oct 30, 2018
David Balduzzi, Karl Tuyls, Julien Perolat, Thore Graepel

* NIPS 2018, final version 

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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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Inequity aversion improves cooperation in intertemporal social dilemmas

Sep 27, 2018
Edward Hughes, Joel Z. Leibo, Matthew G. Phillips, Karl Tuyls, Edgar A. Du√©√Īez-Guzm√°n, Antonio Garc√≠a Casta√Īeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel

* 15 pages, 8 figures 

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Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Jul 03, 2018
Max Jaderberg, Wojciech M. Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C. Rabinowitz, Ari S. Morcos, Avraham Ruderman, Nicolas Sonnerat, Tim Green, Louise Deason, Joel Z. Leibo, David Silver, Demis Hassabis, Koray Kavukcuoglu, Thore Graepel


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Adaptive Mechanism Design: Learning to Promote Cooperation

Jun 11, 2018
Tobias Baumann, Thore Graepel, John Shawe-Taylor


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The Mechanics of n-Player Differentiable Games

Jun 06, 2018
David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, Thore Graepel

* PMLR volume 80, 2018 
* ICML 2018, final version 

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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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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Nov 07, 2017
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel

* Camera-ready copy of NIPS 2017 paper, including appendix 

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A multi-agent reinforcement learning model of common-pool resource appropriation

Sep 06, 2017
Julien Perolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel

* 15 pages, 11 figures 

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Value-Decomposition Networks For Cooperative Multi-Agent Learning

Jun 16, 2017
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel


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Learning Shared Representations in Multi-task Reinforcement Learning

Mar 07, 2016
Diana Borsa, Thore Graepel, John Shawe-Taylor


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The Wreath Process: A totally generative model of geometric shape based on nested symmetries

Jun 09, 2015
Diana Borsa, Thore Graepel, Andrew Gordon

* 10 pages(double-column), 60+ figures 

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A Comparison of learning algorithms on the Arcade Learning Environment

Oct 31, 2014
Aaron Defazio, Thore Graepel


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Compiling Relational Database Schemata into Probabilistic Graphical Models

Dec 05, 2012
Sameer Singh, Thore Graepel

* NIPS 2012 Workshop on Probabilistic Programming 

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