Alert button
Picture for Marc Lanctot

Marc Lanctot

Alert button

Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning

Jun 30, 2022
Julien Perolat, Bart de Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas 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, Remi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls

Figure 1 for Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Figure 2 for Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Figure 3 for Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Figure 4 for Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Viaarxiv icon

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

Jun 12, 2022
Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer

Figure 1 for A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Figure 2 for A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Figure 3 for A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Figure 4 for A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Viaarxiv icon

ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret

Jun 08, 2022
Stephen McAleer, Gabriele Farina, Marc Lanctot, Tuomas Sandholm

Figure 1 for ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret
Figure 2 for ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret
Figure 3 for ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret
Figure 4 for ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret
Viaarxiv icon

Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games

May 31, 2022
Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess

Figure 1 for Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Figure 2 for Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Figure 3 for Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Figure 4 for Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
Viaarxiv icon

Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections

May 24, 2022
Dustin Morrill, Ryan D'Orazio, Marc Lanctot, James R. Wright, Michael Bowling, Amy R. Greenwald

Figure 1 for Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Figure 2 for Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Figure 3 for Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Figure 4 for Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Viaarxiv icon

Anytime PSRO for Two-Player Zero-Sum Games

Jan 28, 2022
Stephen McAleer, Kevin Wang, John Lanier, Marc Lanctot, Pierre Baldi, Tuomas Sandholm, Roy Fox

Figure 1 for Anytime PSRO for Two-Player Zero-Sum Games
Figure 2 for Anytime PSRO for Two-Player Zero-Sum Games
Figure 3 for Anytime PSRO for Two-Player Zero-Sum Games
Figure 4 for Anytime PSRO for Two-Player Zero-Sum Games
Viaarxiv icon

Anytime Optimal PSRO for Two-Player Zero-Sum Games

Jan 19, 2022
Stephen McAleer, Kevin Wang, Marc Lanctot, John Lanier, Pierre Baldi, Roy Fox

Figure 1 for Anytime Optimal PSRO for Two-Player Zero-Sum Games
Figure 2 for Anytime Optimal PSRO for Two-Player Zero-Sum Games
Figure 3 for Anytime Optimal PSRO for Two-Player Zero-Sum Games
Figure 4 for Anytime Optimal PSRO for Two-Player Zero-Sum Games
Viaarxiv icon

Player of Games

Dec 06, 2021
Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, Zach Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling

Figure 1 for Player of Games
Figure 2 for Player of Games
Figure 3 for Player of Games
Figure 4 for Player of Games
Viaarxiv icon

Dynamic population-based meta-learning for multi-agent communication with natural language

Oct 27, 2021
Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou

Figure 1 for Dynamic population-based meta-learning for multi-agent communication with natural language
Figure 2 for Dynamic population-based meta-learning for multi-agent communication with natural language
Figure 3 for Dynamic population-based meta-learning for multi-agent communication with natural language
Figure 4 for Dynamic population-based meta-learning for multi-agent communication with natural language
Viaarxiv icon

Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

Jun 22, 2021
Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel

Figure 1 for Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Figure 2 for Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Figure 3 for Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Figure 4 for Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Viaarxiv icon