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Deep reinforcement learning models the emergent dynamics of human cooperation


Mar 08, 2021
Kevin R. McKee, Edward Hughes, Tina O. Zhu, Martin J. Chadwick, Raphael Koster, Antonio Garcia Castaneda, Charlie Beattie, Thore Graepel, Matt Botvinick, Joel Z. Leibo


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EigenGame Unloaded: When playing games is better than optimizing


Feb 08, 2021
Ian Gemp, Brian McWilliams, Claire Vernade, Thore Graepel


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Open Problems in Cooperative AI


Dec 15, 2020
Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel


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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 Duéñez-Guzmán, 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. Duéñez-Guzmán, Antonio García Castañeda, 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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