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Collaborating with Humans without Human Data

Oct 15, 2021
DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett

* Accepted at NeurIPS 2021 (spotlight) 

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Quantifying environment and population diversity in multi-agent reinforcement learning

Feb 16, 2021
Kevin R. McKee, Joel Z. Leibo, Charlie Beattie, Richard Everett

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Modelling Cooperation in Network Games with Spatio-Temporal Complexity

Feb 13, 2021
Michiel A. Bakker, Richard Everett, Laura Weidinger, Iason Gabriel, William S. Isaac, Joel Z. Leibo, Edward Hughes

* AAMAS 2021 

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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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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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Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

Feb 22, 2018
Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts

* 9 pages, 5 figures, conference submission, University of Oxford 

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Inferring agent objectives at different scales of a complex adaptive system

Dec 04, 2017
Dieter Hendricks, Adam Cobb, Richard Everett, Jonathan Downing, Stephen J. Roberts

* 6 pages, 3 figures, NIPS 2017 Workshop on Learning in the Presence of Strategic Behaviour (MLStrat) 

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