Julien Pérolat

According to our database, Julien Pérolat authored at least 51 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

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 W. Anthony, Julien Pérolat

CoRR, 2023

2022

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments.

Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

CoRR, 2022

Learning Correlated Equilibria in Mean-Field Games.

Paul Muller, Romuald Elie, Mark Rowland, Mathieu Laurière, Julien Pérolat, Sarah Perrin, Matthieu Geist, Georgios Piliouras, Olivier Pietquin, Karl Tuyls

CoRR, 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, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

CoRR, 2022

Developing, evaluating and scaling learning agents in multi-agent environments.

Ian Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar A. Duéñez-Guzmán, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, Siqi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Pérolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

AI Commun., 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO.

Paul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 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

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 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

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Generalization in Mean Field Games by Learning Master Policies.

Sarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Game Plan: What AI can do for Football, and What Football can do for AI.

Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis

J. Artif. Intell. Res., 2021

Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning.

Justin Fu, Andrea Tacchetti, Julien Pérolat, Yoram Bachrach

J. Artif. Intell. Res., 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, Emilio Parisotto, Tom Erez, Yutian Chen, Scott E. Reed, Marcus Hutter, Nando de Freitas, Shane Legg

CoRR, 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

Mean Field Games Flock! The Reinforcement Learning Way.

Sarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

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

Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls

Proceedings of the 38th International Conference on Machine Learning, 2021

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, Karl Tuyls

CoRR, 2020

Navigating the Landscape of Games.

Shayegan Omidshafiei, Karl Tuyls, Wojciech M. Czarnecki, Francisco C. Santos, Mark Rowland, Jerome T. Connor, Daniel Hennes, Paul Muller, Julien Pérolat, Bart De Vylder, Audrunas Gruslys, Rémi Munos

CoRR, 2020

Bounds and dynamics for empirical game theoretic analysis.

Karl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel

Auton. Agents Multi Agent Syst., 2020

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications.

Sarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to Play No-Press Diplomacy with Best Response Policy Iteration.

Thomas W. 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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Fast computation of Nash Equilibria in Imperfect Information Games.

Rémi Munos, Julien Pérolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder, Marc Lanctot, Finbarr Timbers, Daniel Hennes, Shayegan Omidshafiei, Audrunas Gruslys, Mohammad Gheshlaghi Azar, Edward Lockhart, Karl Tuyls

Proceedings of the 37th International Conference on Machine Learning, 2020

A Generalized Training Approach for Multiagent Learning.

Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Pérolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Rémi Munos

Proceedings of the 8th International Conference on Learning Representations, 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

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Foolproof Cooperative Learning.

Alexis Jacq, Julien Pérolat, Matthieu Geist, Olivier Pietquin

Proceedings of The 12th Asian Conference on Machine Learning, 2020

On the Convergence of Model Free Learning in Mean Field Games.

Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

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, 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 W. Anthony, Edward Hughes, Ivo Danihelka, Jonah Ryan-Davis

CoRR, 2019

Approximate Fictitious Play for Mean Field Games.

Romuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin

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

α-Rank: Multi-Agent Evaluation by Evolution.

Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Pérolat, Rémi Munos

CoRR, 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, Kokil Jaidka, Pavan Kapanipathi, Sarah Keren, Seokhwan Kim, Marc Lanctot, Danny Lange, Julian J. McAuley, David R. Martinez, Marwan Mattar, Mausam, Martin Michalowski, Reuth Mirsky, Roozbeh Mottaghi, Joseph C. Osborn, Julien Pérolat, Martin Schmid, Arash Shaban-Nejad, Onn Shehory, Biplav Srivastava, William W. Streilein, Kartik Talamadupula, Julian Togelius, Koichiro Yoshino, Quanshi Zhang, Imed Zitouni

AI Mag., 2019

Multiagent Evaluation under Incomplete Information.

Mark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 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

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Open-ended learning in symmetric zero-sum games.

David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Pérolat, Max Jaderberg, Thore Graepel

Proceedings of the 36th International Conference on Machine Learning, 2019

Malthusian Reinforcement Learning.

Joel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

2018

Playing the Game of Universal Adversarial Perturbations.

Julien Pérolat, Mateusz Malinowski, Bilal Piot, Olivier Pietquin

CoRR, 2018

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments.

Sriram Srinivasan, Marc Lanctot, Vinícius Flores Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, Michael Bowling

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Re-evaluating evaluation.

David Balduzzi, Karl Tuyls, Julien Pérolat, Thore Graepel

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Generalised Method for Empirical Game Theoretic Analysis.

Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Actor-Critic Fictitious Play in Simultaneous Move Multistage Games.

Julien Pérolat, Bilal Piot, Olivier Pietquin

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

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

Julien Pérolat

PhD thesis, 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

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning Nash Equilibrium for General-Sum Markov Games from Batch Data.

Julien Pérolat, Florian Strub, Bilal Piot, Olivier Pietquin

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Softened Approximate Policy Iteration for Markov Games.

Julien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin

Proceedings of the 33nd International Conference on Machine Learning, 2016

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games.

Julien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Generalizing the Wilcoxon rank-sum test for interval data.

Julien Pérolat, Inés Couso, Kevin Loquin, Olivier Strauss

Int. J. Approx. Reason., 2015

Human-Machine Dialogue as a Stochastic Game.

Merwan Barlier, Julien Pérolat, Romain Laroche, Olivier Pietquin

Proceedings of the SIGDIAL 2015 Conference, 2015

Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games.

Julien Pérolat, Bruno Scherrer, Bilal Piot, Olivier Pietquin

Proceedings of the 32nd International Conference on Machine Learning, 2015