The Vanderbilt Perioperative Information Management System Forms Foundation for Outcomes Management. - Michael Higgins; Charles Beattie; Paul St. Jaques; Nimesh P. Patel; Joshua Yarborough; Yu Show Tsai (AMIA, 1997)

Massively Parallel Methods for Deep Reinforcement Learning. - Arun Nair; Praveen Srinivasan; Sam Blackwell; Cagdas Alcicek; Rory Fearon; Alessandro De Maria; Vedavyas Panneershelvam; Mustafa Suleyman; Charles Beattie; Stig Petersen; Shane Legg; Volodymyr Mnih; et al. (CoRR, 2015)

Human-level control through deep reinforcement learning. - Volodymyr Mnih; Koray Kavukcuoglu; David Silver; Andrei A. Rusu; Joel Veness; Marc G. Bellemare; Alex Graves; Martin A. Riedmiller; Andreas Fidjeland; Georg Ostrovski; Stig Petersen; Charles Beattie; et al. (Nat., 2015)

DeepMind Lab. - Charles Beattie; Joel Z. Leibo; Denis Teplyashin; Tom Ward; Marcus Wainwright; Heinrich Küttler; Andrew Lefrancq; Simon Green; Víctor Valdés; Amir Sadik; Julian Schrittwieser; Keith Anderson; et al. (CoRR, 2016)

A multi-agent reinforcement learning model of common-pool resource appropriation. - Julien Pérolat; Joel Z. Leibo; Vinícius Flores Zambaldi; Charles Beattie; Karl Tuyls; Thore Graepel (NIPS, 2017)

Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents. - 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; et al. (CoRR, 2018)

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning. - Max Jaderberg; Wojciech M. Czarnecki; Iain Dunning; Luke Marris; Guy Lever; Antonio García Castañeda; Charles Beattie; Neil C. Rabinowitz; Ari S. Morcos; Avraham Ruderman; Nicolas Sonnerat; Tim Green; et al. (CoRR, 2018)

Vector-based navigation using grid-like representations in artificial agents. - Andrea Banino; Caswell Barry; Benigno Uria; Charles Blundell; Timothy P. Lillicrap; Piotr Mirowski; Alexander Pritzel; Martin J. Chadwick; Thomas Degris; Joseph Modayil; Greg Wayne; Hubert Soyer; et al. (Nat., 2018)

DeepMind Lab2D. - Charles Beattie; Thomas Köppe; Edgar A. Duéñez-Guzmán; Joel Z. Leibo (CoRR, 2020)

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot. - Joel Z. Leibo; Edgar A. Duéñez-Guzmán; Alexander Vezhnevets; John P. Agapiou; Peter Sunehag; Raphael Koster; Jayd Matyas; Charlie Beattie; Igor Mordatch; Thore Graepel (ICML, 2021)

Quantifying environment and population diversity in multi-agent reinforcement learning. - Kevin R. McKee; Joel Z. Leibo; Charlie Beattie; Richard Everett 001 (CoRR, 2021)

Deep reinforcement learning models the emergent dynamics of human cooperation. - Kevin R. McKee; Edward Hughes 001; Tina O. Zhu; Martin J. Chadwick; Raphael Koster; Antonio García Castañeda; Charlie Beattie; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2021)

Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs. - Dan Rosenbaum; Marta Garnelo; Michal Zielinski; Charlie Beattie; Ellen Clancy; Andrea Huber; Pushmeet Kohli; Andrew W. Senior; John Jumper; Carl Doersch; S. M. Ali Eslami; Olaf Ronneberger; et al. (CoRR, 2021)

Quantifying the effects of environment and population diversity in multi-agent reinforcement learning. - Kevin R. McKee; Joel Z. Leibo; Charlie Beattie; Richard Everett 001 (Auton. Agents Multi Agent Syst., 2022)

Modeling human reputation-seeking behavior in a spatio-temporally complex public good provision game. - Edward Hughes 001; Tina O. Zhu; Martin J. Chadwick; Raphael Koster; Antonio García Castañeda; Charles Beattie; Thore Graepel; Matthew M. Botvinick; Joel Z. Leibo (CoRR, 2025)