Julien Pérolat - researchr alias

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Viewing Publication 1 - 35 from 35

2022

Solving N-Player Dynamic Routing Games with Congestion: A Mean-Field ApproachTheophile Cabannes, Mathieu Laurière, Julien Pérolat, Raphaël Marinier, Sertan Girgin, Sarah Perrin, Olivier Pietquin, Alexandre M. Bayen, Eric Goubault, Romuald Elie. atal 2022: 1557-1559 [doi]

Concave Utility Reinforcement Learning: The Mean-field Game ViewpointMatthieu Geist, Julien Pérolat, Mathieu Laurière, Romuald Elie, Sarah Perrin, Olivier Bachem, Rémi Munos, Olivier Pietquin. atal 2022: 489-497 [doi]

Scalable Deep Reinforcement Learning Algorithms for Mean Field GamesMathieu Laurière, Sarah Perrin, Sertan Girgin, Paul Muller, Ayush Jain, Theophile Cabannes, Georgios Piliouras, Julien Pérolat, Romuald Elie, Olivier Pietquin, Matthieu Geist. icml 2022: 12078-12095 [doi]

Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSROPaul Muller, Mark Rowland, Romuald Elie, Georgios Piliouras, Julien Pérolat, Mathieu Laurière, Raphaël Marinier, Olivier Pietquin, Karl Tuyls. atal 2022: 926-934 [doi]

Generalization in Mean Field Games by Learning Master PoliciesSarah Perrin, Mathieu Laurière, Julien Pérolat, Romuald Élie, Matthieu Geist, Olivier Pietquin. AAAI 2022: 9413-9421 [doi]

Scaling Mean Field Games by Online Mirror DescentJulien Pérolat, Sarah Perrin, Romuald Elie, Mathieu Laurière, Georgios Piliouras, Matthieu Geist, Karl Tuyls, Olivier Pietquin. atal 2022: 1028-1037 [doi]

2021

Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement LearningJustin Fu, Andrea Tacchetti, Julien Pérolat, Yoram Bachrach. jair, 71:925-951, 2021. [doi]

Mean Field Games Flock! The Reinforcement Learning WaySarah Perrin, Mathieu Laurière, Julien Pérolat, Matthieu Geist, Romuald Élie, Olivier Pietquin. IJCAI 2021: 356-362 [doi]

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via RegularizationJulien 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. icml 2021: 8525-8535 [doi]

2020

Learning to Play No-Press Diplomacy with Best Response Policy IterationThomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett 0001, Satinder Singh, Thore Graepel, Yoram Bachrach. nips 2020: [doi]

On the Convergence of Model Free Learning in Mean Field GamesRomuald Elie, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Olivier Pietquin. AAAI 2020: 7143-7150 [doi]

Neural Replicator Dynamics: Multiagent Learning via Hedging Policy GradientsDaniel Hennes, Dustin Morrill, Shayegan Omidshafiei, Rémi Munos, Julien Pérolat, Marc Lanctot, Audrunas Gruslys, Jean-Baptiste Lespiau, Paavo Parmas, Edgar A. Duéñez-Guzmán, Karl Tuyls. atal 2020: 492-501 [doi]

Foolproof Cooperative LearningAlexis Jacq, Julien Pérolat, Matthieu Geist, Olivier Pietquin. acml 2020: 401-416 [doi]

A Generalized Training Approach for Multiagent LearningPaul 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. iclr 2020: [doi]

Fast computation of {NRémi 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. icml 2020: 7119-7129 [doi]

Fictitious Play for Mean Field Games: Continuous Time Analysis and ApplicationsSarah Perrin, Julien Pérolat, Mathieu Laurière, Matthieu Geist, Romuald Elie, Olivier Pietquin. nips 2020: [doi]

Bounds and dynamics for empirical game theoretic analysisKarl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel. AAMAS, 34(1):7, 2020. [doi]

2019

Open-ended learning in symmetric zero-sum gamesDavid Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki 0001, Julien Pérolat, Max Jaderberg, Thore Graepel. icml 2019: 434-443 [doi]

Malthusian Reinforcement LearningJoel Z. Leibo, Julien Pérolat, Edward Hughes, Steven Wheelwright, Adam H. Marblestone, Edgar A. Duéñez-Guzmán, Peter Sunehag, Iain Dunning, Thore Graepel. atal 2019: 1099-1107 [doi]

Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability DescentEdward Lockhart, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr Timbers, Karl Tuyls. IJCAI 2019: 464-470 [doi]

Multiagent Evaluation under Incomplete InformationMark Rowland, Shayegan Omidshafiei, Karl Tuyls, Julien Pérolat, Michal Valko, Georgios Piliouras, Rémi Munos. nips 2019: 12270-12282 [doi]

2018

2017

2016

On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov GamesJulien Pérolat, Bilal Piot, Bruno Scherrer, Olivier Pietquin. aistats 2016: 893-901 [doi]

Softened Approximate Policy Iteration for Markov GamesJulien Pérolat, Bilal Piot, Matthieu Geist, Bruno Scherrer, Olivier Pietquin. icml 2016: 1860-1868 [doi]

2015

Human-Machine Dialogue as a Stochastic GameMerwan Barlier, Julien Pérolat, Romain Laroche, Olivier Pietquin. sigdial 2015: 2-11 [doi]

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