Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games. - Julien Pérolat; Bruno Scherrer; Bilal Piot; Olivier Pietquin (ICML, 2015)

Human-Machine Dialogue as a Stochastic Game. - Merwan Barlier; Julien Pérolat; Romain Laroche; Olivier Pietquin (SIGDIAL Conference, 2015)

Generalizing the Wilcoxon rank-sum test for interval data. - Julien Pérolat; Inés Couso; Kevin Loquin; Olivier Strauss (Int. J. Approx. Reason., 2015)

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games. - Julien Pérolat; Bilal Piot; Bruno Scherrer; Olivier Pietquin (AISTATS, 2016)

Softened Approximate Policy Iteration for Markov Games. - Julien Pérolat; Bilal Piot; Matthieu Geist; Bruno Scherrer; Olivier Pietquin (ICML, 2016)

Learning Nash Equilibrium for General-Sum Markov Games from Batch Data. - Julien Pérolat; Florian Strub; Bilal Piot; Olivier Pietquin (AISTATS, 2017)

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. - Marc Lanctot; Vinícius Flores Zambaldi; Audrunas Gruslys; Angeliki Lazaridou; Karl Tuyls; Julien Pérolat; David Silver; Thore Graepel (NIPS, 2017)

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)

Symmetric Decomposition of Asymmetric Games. - Karl Tuyls; Julien Pérolat; Marc Lanctot; Georg Ostrovski; Rahul Savani; Joel Z. Leibo; Toby Ord; Thore Graepel; Shane Legg (CoRR, 2017)

Reinforcement Learning: The Multi-Player Case. (Apprentissage par Renforcement: Le Cas Multijoueur). - Julien Pérolat (2017)

Actor-Critic Fictitious Play in Simultaneous Move Multistage Games. - Julien Pérolat; Bilal Piot; Olivier Pietquin (AISTATS, 2018)

A Generalised Method for Empirical Game Theoretic Analysis. - Karl Tuyls; Julien Pérolat; Marc Lanctot; Joel Z. Leibo; Thore Graepel (AAMAS, 2018)

Re-evaluating evaluation. - David Balduzzi; Karl Tuyls; Julien Pérolat; Thore Graepel (NeurIPS, 2018)

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. - Sriram Srinivasan 005; Marc Lanctot; Vinícius Flores Zambaldi; Julien Pérolat; Karl Tuyls; Rémi Munos; Michael Bowling (NeurIPS, 2018)

Playing the Game of Universal Adversarial Perturbations. - Julien Pérolat; Mateusz Malinowski; Bilal Piot; Olivier Pietquin (CoRR, 2018)

Malthusian Reinforcement Learning. - Joel Z. Leibo; Julien Pérolat; Edward Hughes 001; Steven Wheelwright; Adam H. Marblestone; Edgar A. Duéñez-Guzmán; Peter Sunehag; Iain Dunning; Thore Graepel (AAMAS, 2019)

Open-ended learning in symmetric zero-sum games. - David Balduzzi; Marta Garnelo; Yoram Bachrach; Wojciech Czarnecki 001; Julien Pérolat; Max Jaderberg; Thore Graepel (ICML, 2019)

Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent. - Edward Lockhart; Marc Lanctot; Julien Pérolat; Jean-Baptiste Lespiau; Dustin Morrill; Finbarr Timbers; Karl Tuyls (IJCAI, 2019)

Multiagent Evaluation under Incomplete Information. - Mark Rowland 001; Shayegan Omidshafiei; Karl Tuyls; Julien Pérolat; Michal Valko; Georgios Piliouras; Rémi Munos (NeurIPS, 2019)

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence. - Guy Barash; Mauricio Castillo-Effen; Niyati Chhaya; Peter Clark; Huáscar Espinoza; Eitan Farchi; Christopher W. Geib; Odd Erik Gundersen; Seán Ó hÉigeartaigh; José Hernández-Orallo; Chiori Hori; Xiaowei Huang 001; et al. (AI Mag., 2019)

α-Rank: Multi-Agent Evaluation by Evolution. - Shayegan Omidshafiei; Christos H. Papadimitriou; Georgios Piliouras; Karl Tuyls; Mark Rowland 001; Jean-Baptiste Lespiau; Wojciech M. Czarnecki; Marc Lanctot; Julien Pérolat; Rémi Munos (CoRR, 2019)

Neural Replicator Dynamics. - Shayegan Omidshafiei; Daniel Hennes; Dustin Morrill; Rémi Munos; Julien Pérolat; Marc Lanctot; Audrunas Gruslys; Jean-Baptiste Lespiau; Karl Tuyls (CoRR, 2019)

Approximate Fictitious Play for Mean Field Games. - Romuald Elie; Julien Pérolat; Mathieu Laurière; Matthieu Geist; Olivier Pietquin (CoRR, 2019)

OpenSpiel: A Framework for Reinforcement Learning in Games. - Marc Lanctot; Edward Lockhart; Jean-Baptiste Lespiau; Vinícius Flores Zambaldi; Satyaki Upadhyay; Julien Pérolat; Sriram Srinivasan 005; Finbarr Timbers; Karl Tuyls; Shayegan Omidshafiei; Daniel Hennes; Dustin Morrill; et al. (CoRR, 2019)

On the Convergence of Model Free Learning in Mean Field Games. - Romuald Elie; Julien Pérolat; Mathieu Laurière; Matthieu Geist; Olivier Pietquin (AAAI, 2020)

Foolproof Cooperative Learning. - Alexis Jacq; Julien Pérolat; Matthieu Geist; Olivier Pietquin (ACML, 2020)

Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. - Daniel Hennes; Dustin Morrill; Shayegan Omidshafiei; Rémi Munos; Julien Pérolat; Marc Lanctot; Audrunas Gruslys; Jean-Baptiste Lespiau; Paavo Parmas; Edgar A. Duéñez-Guzmán; Karl Tuyls (AAMAS, 2020)

A Generalized Training Approach for Multiagent Learning. - Paul Muller; Shayegan Omidshafiei; Mark Rowland 001; Karl Tuyls; Julien Pérolat; Siqi Liu 002; Daniel Hennes; Luke Marris; Marc Lanctot; Edward Hughes 001; Zhe Wang 055; Guy Lever; et al. (ICLR, 2020)

Fast computation of Nash Equilibria in Imperfect Information Games. - Rémi Munos; Julien Pérolat; Jean-Baptiste Lespiau; Mark Rowland 001; Bart De Vylder; Marc Lanctot; Finbarr Timbers; Daniel Hennes; Shayegan Omidshafiei; Audrunas Gruslys; Mohammad Gheshlaghi Azar; Edward Lockhart; et al. (ICML, 2020)

Learning to Play No-Press Diplomacy with Best Response Policy Iteration. - Thomas W. Anthony 001; Tom Eccles; Andrea Tacchetti; János Kramár; Ian Gemp; Thomas C. Hudson; Nicolas Porcel; Marc Lanctot; Julien Pérolat; Richard Everett 001; Satinder Singh 001; Thore Graepel; et al. (NeurIPS, 2020)

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications. - Sarah Perrin; Julien Pérolat; Mathieu Laurière; Matthieu Geist; Romuald Elie; Olivier Pietquin (NeurIPS, 2020)

Bounds and dynamics for empirical game theoretic analysis. - Karl Tuyls; Julien Pérolat; Marc Lanctot; Edward Hughes 001; Richard Everett 001; Joel Z. Leibo; Csaba Szepesvári; Thore Graepel (Auton. Agents Multi Agent Syst., 2020)

Navigating the Landscape of Games. - Shayegan Omidshafiei; Karl Tuyls; Wojciech M. Czarnecki; Francisco C. Santos; Mark Rowland 001; Jerome T. Connor; Daniel Hennes; Paul Muller; Julien Pérolat; Bart De Vylder; Audrunas Gruslys; Rémi Munos (CoRR, 2020)

The Advantage Regret-Matching Actor-Critic. - Audrunas Gruslys; Marc Lanctot; Rémi Munos; Finbarr Timbers; Martin Schmid; Julien Pérolat; Dustin Morrill; Vinícius Flores Zambaldi; Jean-Baptiste Lespiau; John Schultz; Mohammad Gheshlaghi Azar; Michael Bowling; et al. (CoRR, 2020)

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. - Julien Pérolat; Rémi Munos; Jean-Baptiste Lespiau; Shayegan Omidshafiei; Mark Rowland 001; Pedro A. Ortega; Neil Burch; Thomas W. Anthony 001; David Balduzzi; Bart De Vylder; Georgios Piliouras; Marc Lanctot; et al. (ICML, 2021)

Mean Field Games Flock! The Reinforcement Learning Way. - Sarah Perrin; Mathieu Laurière; Julien Pérolat; Matthieu Geist; Romuald Élie; Olivier Pietquin (IJCAI, 2021)

Scaling up Mean Field Games with Online Mirror Descent. - Julien Pérolat; Sarah Perrin; Romuald Elie; Mathieu Laurière; Georgios Piliouras; Matthieu Geist; Karl Tuyls; Olivier Pietquin (CoRR, 2021)

Shaking the foundations: delusions in sequence models for interaction and control. - Pedro A. Ortega; Markus Kunesch; Grégoire Delétang; Tim Genewein; Jordi Grau-Moya; Joel Veness; Jonas Buchli; Jonas Degrave; Bilal Piot; Julien Pérolat; Tom Everitt; Corentin Tallec; et al. (CoRR, 2021)

Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning. - Justin Fu; Andrea Tacchetti; Julien Pérolat; Yoram Bachrach (J. Artif. Intell. Res., 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

Generalization in Mean Field Games by Learning Master Policies. - Sarah Perrin; Mathieu Laurière; Julien Pérolat; Romuald Élie; Matthieu Geist; Olivier Pietquin (AAAI, 2022)

Solving N-Player Dynamic Routing Games with Congestion: A Mean-Field Approach. - Theophile Cabannes; Mathieu Laurière; Julien Pérolat; Raphaël Marinier; Sertan Girgin; Sarah Perrin; Olivier Pietquin; Alexandre M. Bayen; Eric Goubault; Romuald Elie (AAMAS, 2022)

Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint. - Matthieu Geist; Julien Pérolat; Mathieu Laurière; Romuald Elie; Sarah Perrin; Olivier Bachem; Rémi Munos; Olivier Pietquin (AAMAS, 2022)

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO. - Paul Muller; Mark Rowland 001; Romuald Elie; Georgios Piliouras; Julien Pérolat; Mathieu Laurière; Raphaël Marinier; Olivier Pietquin; Karl Tuyls (AAMAS, 2022)

Scaling Mean Field Games by Online Mirror Descent. - Julien Pérolat; Sarah Perrin; Romuald Elie; Mathieu Laurière; Georgios Piliouras; Matthieu Geist; Karl Tuyls; Olivier Pietquin (AAMAS, 2022)

Scalable Deep Reinforcement Learning Algorithms for Mean Field Games. - Mathieu Laurière; Sarah Perrin; Sertan Girgin; Paul Muller; Ayush Jain; Theophile Cabannes; Georgios Piliouras; Julien Pérolat; Romuald Elie; Olivier Pietquin; Matthieu Geist (ICML, 2022)

Developing, evaluating and scaling learning agents in multi-agent environments. - Ian Gemp; Thomas W. Anthony 001; Yoram Bachrach; Avishkar Bhoopchand; Kalesha Bullard; Jerome T. Connor; Vibhavari Dasagi; Bart De Vylder; Edgar A. Duéñez-Guzmán; Romuald Elie; Richard Everett 001; Daniel Hennes; et al. (AI Commun., 2022)

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas W. Anthony 001; Stephen McAleer; Romuald Elie; et al. (CoRR, 2022)

Learning Correlated Equilibria in Mean-Field Games. - Paul Muller; Romuald Elie; Mark Rowland 001; Mathieu Laurière; Julien Pérolat; Sarah Perrin; Matthieu Geist; Georgios Piliouras; Olivier Pietquin; Karl Tuyls (CoRR, 2022)

Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning". - Julien Pérolat; Bart De Vylder; Daniel Hennes; Eugene Tarassov; Florian Strub; Vincent de Boer; Paul Muller; Jerome T. Connor; Neil Burch; Thomas Anthony 001; Stephen McAleer; Romuald Elie; et al. (Zenodo, 2022)

Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. - Marc Lanctot; John Schultz; Neil Burch; Max Olan Smith; Daniel Hennes; Thomas Anthony 001; Julien Pérolat (Trans. Mach. Learn. Res., 2023)