Lanctot, Marc

40 publications

IJCAI 2025 Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman

ICML 2025 Mastering Board Games by External and Internal Planning with Language Models John Schultz, Jakub Adamek, Matej Jusup, Marc Lanctot, Michael Kaisers, Sarah Perrin, Daniel Hennes, Jeremy Shar, Cannada A. Lewis, Anian Ruoss, Tom Zahavy, Petar Veličković, Laurel Prince, Satinder Singh, Eric Malmi, Nenad Tomasev

ICLR 2025 Re-Evaluating Open-Ended Evaluation of Large Language Models Siqi Liu, Ian Gemp, Luke Marris, Georgios Piliouras, Nicolas Heess, Marc Lanctot

AAAI 2024 Learning Not to Regret David Sychrovsky, Michal Sustr, Elnaz Davoodi, Michael Bowling, Marc Lanctot, Martin Schmid

ICMLW 2024 Steering Language Models with Game-Theoretic Solvers Ian Gemp, Roma Patel, Yoram Bachrach, Marc Lanctot, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, Siqi Liu, Karl Tuyls

ICLR 2023 A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games Samuel Sokota, Ryan D'Orazio, J Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer

ICLR 2023 ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret Stephen Marcus McAleer, Gabriele Farina, Marc Lanctot, Tuomas Sandholm

TMLR 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 Anthony, Julien Perolat

NeurIPSW 2022 A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games Samuel Sokota, Ryan D'Orazio, J Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer

IJCAI 2022 Approximate Exploitability: Learning a Best Response Finbarr Timbers, Nolan Bard, Edward Lockhart, Marc Lanctot, Martin Schmid, Neil Burch, Julian Schrittwieser, Thomas Hubert, Michael Bowling

NeurIPSW 2022 ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret Stephen Marcus McAleer, Gabriele Farina, Marc Lanctot, Tuomas Sandholm

ICML 2022 Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-Sum Games Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess

NeurIPS 2021 Dynamic Population-Based Meta-Learning for Multi-Agent Communication with Natural Language Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou

ICML 2021 Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games Dustin Morrill, Ryan D’Orazio, Marc Lanctot, James R Wright, Michael Bowling, Amy R Greenwald

ICML 2021 From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization Julien Perolat, Remi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro Ortega, Neil Burch, Thomas Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls

AAAI 2021 Hindsight and Sequential Rationality of Correlated Play Dustin Morrill, Ryan D'Orazio, Reca Sarfati, Marc Lanctot, James R. Wright, Amy R. Greenwald, Michael Bowling

ICLRW 2021 Meta Learning for Multi-Agent Communication Abhinav Gupta, Angeliki Lazaridou, Marc Lanctot

ICML 2021 Multi-Agent Training Beyond Zero-Sum with Correlated Equilibrium Meta-Solvers Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel

AAAI 2021 Solving Common-Payoff Games with Approximate Policy Iteration Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot

ICLR 2020 A Generalized Training Approach for Multiagent Learning Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Siqi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos

ICML 2020 Fast Computation of Nash Equilibria in Imperfect Information Games Remi Munos, Julien Perolat, 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

NeurIPS 2020 Learning to Play No-Press Diplomacy with Best Response Policy Iteration Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas Hudson, Nicolas Porcel, Marc Lanctot, Julien Perolat, Richard Everett, Satinder P. Singh, Thore Graepel, Yoram Bachrach

IJCAI 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

AAAI 2019 Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling

NeurIPS 2018 Actor-Critic Policy Optimization in Partially Observable Multiagent Environments Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Perolat, Karl Tuyls, Remi Munos, Michael Bowling

AAAI 2018 Deep Q-Learning from Demonstrations Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys

ICLR 2018 Emergent Communication Through Negotiation Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z Leibo, Karl Tuyls, Stephen Clark

NeurIPS 2017 A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Perolat, David Silver, Thore Graepel

ICML 2016 Dueling Network Architectures for Deep Reinforcement Learning Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot, Nando Freitas

NeurIPS 2016 Memory-Efficient Backpropagation Through Time Audrunas Gruslys, Remi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves

ICML 2015 Fictitious Self-Play in Extensive-Form Games Johannes Heinrich, Marc Lanctot, David Silver

NeurIPS 2013 Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky

IJCAI 2013 Monte Carlo *-Minimax Search Marc Lanctot, Abdallah Saffidine, Joel Veness, Christopher Archibald, Mark H. M. Winands

IJCAI 2013 Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel Marc Lanctot, Viliam Lisý, Mark H. M. Winands

NeurIPS 2012 Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions Neil Burch, Marc Lanctot, Duane Szafron, Richard G. Gibson

AAAI 2012 Generalized Sampling and Variance in Counterfactual Regret Minimization Richard G. Gibson, Marc Lanctot, Neil Burch, Duane Szafron, Michael Bowling

ICML 2012 No-Regret Learning in Extensive-Form Games with Imperfect Recall Marc Lanctot, Richard G. Gibson, Neil Burch, Michael Bowling

JAIR 2011 Computing Approximate Nash Equilibria and Robust Best-Responses Using Sampling Marc J. V. Ponsen, Steven de Jong, Marc Lanctot

NeurIPS 2011 Variance Reduction in Monte-Carlo Tree Search Joel Veness, Marc Lanctot, Michael Bowling

NeurIPS 2009 Monte Carlo Sampling for Regret Minimization in Extensive Games Marc Lanctot, Kevin Waugh, Martin Zinkevich, Michael Bowling