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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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The Advantage Regret-Matching Actor-Critic

Aug 27, 2020
Audrūnas Gruslys, Marc Lanctot, Rémi Munos, Finbarr Timbers, Martin Schmid, Julien Perolat, Dustin Morrill, Vinicius Zambaldi, Jean-Baptiste Lespiau, John Schultz, Mohammad Gheshlaghi Azar, Michael Bowling, Karl Tuyls


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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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Approximate exploitability: Learning a best response in large games

Apr 20, 2020
Finbarr Timbers, Edward Lockhart, Martin Schmid, Marc Lanctot, Michael Bowling


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From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization

Feb 19, 2020
Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls

* 43 pages 

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OpenSpiel: A Framework for Reinforcement Learning in Games

Oct 10, 2019
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinicius Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Paul Muller, Timo Ewalds, Ryan Faulkner, János Kramár, Bart De Vylder, Brennan Saeta, James Bradbury, David Ding, Sebastian Borgeaud, Matthew Lai, Julian Schrittwieser, Thomas Anthony, Edward Hughes, Ivo Danihelka, Jonah Ryan-Davis


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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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Neural Replicator Dynamics

Jun 01, 2019
Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Remi Munos, Julien Perolat, Marc Lanctot, Audrunas Gruslys, Jean-Baptiste Lespiau, Karl Tuyls


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Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent

Mar 21, 2019
Edward Lockhart, Marc Lanctot, Julien PĂ©rolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr Timbers, Karl Tuyls

* 11 pages, 1 figure 

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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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The Hanabi Challenge: A New Frontier for AI Research

Feb 01, 2019
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling

* 37 pages, 5 figures, submitted to Artificial Intelligence 

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Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

Oct 21, 2018
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Perolat, Karl Tuyls, Remi Munos, Michael Bowling

* NIPS 2018 

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Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines

Sep 09, 2018
Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling


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Emergent Communication through Negotiation

Apr 11, 2018
Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, Stephen Clark

* Published as a conference paper at ICLR 2018 

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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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Deep Q-learning from Demonstrations

Nov 22, 2017
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrunas Gruslys

* Published at AAAI 2018. Previously on arxiv as "Learning from Demonstrations for Real World Reinforcement Learning" 

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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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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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Memory-Efficient Backpropagation Through Time

Jun 10, 2016
Audrūnas Gruslys, Remi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves


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Convolution by Evolution: Differentiable Pattern Producing Networks

Jun 08, 2016
Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra


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Dueling Network Architectures for Deep Reinforcement Learning

Apr 05, 2016
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

* 15 pages, 5 figures, and 5 tables 

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Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups

Jun 19, 2014
Marc Lanctot, Mark H. M. Winands, Tom Pepels, Nathan R. Sturtevant

* 24 pages, 7 figures, 9 tables, expanded version of paper presented at IEEE Conference on Computational Intelligence and Games (CIG) 2014 conference 

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No-Regret Learning in Extensive-Form Games with Imperfect Recall

May 03, 2012
Marc Lanctot, Richard Gibson, Neil Burch, Martin Zinkevich, Michael Bowling

* 21 pages, 4 figures, expanded version of article to appear in Proceedings of the Twenty-Ninth International Conference on Machine Learning 

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