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Navigating the Landscape of Games


May 04, 2020
Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome Connor, Daniel Hennes, Paul Muller, Julien Perolat, Bart De Vylder, Audrunas Gruslys, 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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Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents


Feb 04, 2018
Joel Z. Leibo, Cyprien de Masson d'Autume, Daniel Zoran, David Amos, Charles Beattie, Keith Anderson, Antonio García Castañeda, Manuel Sanchez, Simon Green, Audrunas Gruslys, Shane Legg, Demis Hassabis, Matthew M. Botvinick

* 28 pages, 11 figures 

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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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