Marc Lanctot

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 87

Top Venues

CoRR

AAMAS

ICML

AAAI

Publications

David Sychrovsky, Michal Sustr, Elnaz Davoodi, Michael Bowling, Marc Lanctot, Martin Schmid
Learning Not to Regret. AAAI (2024)
Ian Gemp, Marc Lanctot, Luke Marris, Yiran Mao, Edgar A. Duéñez-Guzmán, Sarah Perrin, Andras Gyorgy, Romuald Elie, Georgios Piliouras, Michael Kaisers, Daniel Hennes, Kalesha Bullard, Kate Larson, Yoram Bachrach
Approximating the Core via Iterative Coalition Sampling. AAMAS (2024)
Luca D'Amico-Wong, Hugh Zhang, Marc Lanctot, David C. Parkes
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization. CoRR (2024)
Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess
Neural Population Learning beyond Symmetric Zero-sum Games. CoRR (2024)
Ian Gemp, Marc Lanctot, Luke Marris, Yiran Mao, Edgar A. Duéñez-Guzmán, Sarah Perrin, Andras Gyorgy, Romuald Elie, Georgios Piliouras, Michael Kaisers, Daniel Hennes, Kalesha Bullard, Kate Larson, Yoram Bachrach
Approximating the Core via Iterative Coalition Sampling. CoRR (2024)
Ian Gemp, Yoram Bachrach, Marc Lanctot, Roma Patel, Vibhavari Dasagi, Luke Marris, Georgios Piliouras, Siqi Liu, Karl Tuyls
States as Strings as Strategies: Steering Language Models with Game-Theoretic Solvers. CoRR (2024)
Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess
Neural Population Learning beyond Symmetric Zero-Sum Games. AAMAS (2024)
Marc Lanctot, Kate Larson, Yoram Bachrach, Luke Marris, Zun Li, Avishkar Bhoopchand, Thomas W. Anthony, Brian Tanner, Anna Koop
Evaluating Agents using Social Choice Theory. CoRR (2023)
David Sychrovsky, Michal Sustr, Elnaz Davoodi, Marc Lanctot, Martin Schmid
Learning not to Regret. CoRR (2023)
Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Pérolat
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Kate Larson, Yoram Bachrach, Michael P. Wellman, Paul Muller
Search-Improved Game-Theoretic Multiagent Reinforcement Learning in General and Negotiation Games. AAMAS (2023)
Stephen Marcus McAleer, Gabriele Farina, Marc Lanctot, Tuomas Sandholm
ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret. ICLR (2023)
Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas W. Anthony, Julien Pérolat
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. CoRR (2023)
Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. ICLR (2023)
Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning. CoRR (2023)
Samuel Sokota, Ryan D'Orazio, J. Zico Kolter, Nicolas Loizou, Marc Lanctot, Ioannis Mitliagkas, Noam Brown, Christian Kroer
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games. CoRR (2022)
Stephen McAleer, Kevin Wang, John B. Lanier, Marc Lanctot, Pierre Baldi, Tuomas Sandholm, Roy Fox
Anytime PSRO for Two-Player Zero-Sum Games. CoRR (2022)
Stephen McAleer, Gabriele Farina, Marc Lanctot, Tuomas Sandholm
ESCHER: Eschewing Importance Sampling in Games by Computing a History Value Function to Estimate Regret. CoRR (2022)
Ian M. 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
Developing, evaluating and scaling learning agents in multi-agent environments. AI Commun. 35 (4) (2022)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. AAMAS (2022)
Finbarr Timbers, Nolan Bard, Edward Lockhart, Marc Lanctot, Martin Schmid, Neil Burch, Julian Schrittwieser, Thomas Hubert, Michael Bowling
Approximate Exploitability: Learning a Best Response. IJCAI (2022)
Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. CoRR (2022)
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
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR (2022)
Luke Marris, Marc Lanctot, Ian Gemp, Shayegan Omidshafiei, Stephen McAleer, Jerome T. Connor, Karl Tuyls, Thore Graepel
Game Theoretic Rating in N-player general-sum games with Equilibria. CoRR (2022)
Ian M. Gemp, Thomas W. Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome T. Connor, Vibhavari Dasagi, Bart De Vylder, Edgar 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
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments. CoRR (2022)
Siqi Liu, Marc Lanctot, Luke Marris, Nicolas Heess
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games. ICML (2022)
Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers. ICML (2021)
Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Joshua Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, Zach Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling
Player of Games. CoRR (2021)
Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou
Dynamic population-based meta-learning for multi-agent communication with natural language. NeurIPS (2021)
Dustin Morrill, Ryan D'Orazio, Marc Lanctot, James R. Wright, Michael Bowling, Amy R. Greenwald
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games. ICML (2021)
Abhinav Gupta, Marc Lanctot, Angeliki Lazaridou
Dynamic population-based meta-learning for multi-agent communication with natural language. CoRR (2021)
Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot
Solving Common-Payoff Games with Approximate Policy Iteration. AAAI (2021)
Dustin Morrill, Ryan D'Orazio, Reca Sarfati, Marc Lanctot, James R. Wright, Amy R. Greenwald, Michael Bowling
Hindsight and Sequential Rationality of Correlated Play. AAAI (2021)
Ian M. Gemp, Rahul Savani, Marc Lanctot, Yoram Bachrach, Thomas W. Anthony, Richard Everett, Andrea Tacchetti, Tom Eccles, János Kramár
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. CoRR (2021)
Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot
Solving Common-Payoff Games with Approximate Policy Iteration. CoRR (2021)
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
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML (2021)
Dustin Morrill, Ryan D'Orazio, Marc Lanctot, James R. Wright, Michael Bowling, Amy Greenwald
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games. CoRR (2021)
Michal Sustr, Martin Schmid, Matej Moravcík, Neil Burch, Marc Lanctot, Michael Bowling
Sound Algorithms in Imperfect Information Games. AAMAS (2021)
Luke Marris, Paul Muller, Marc Lanctot, Karl Tuyls, Thore Graepel
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers. CoRR (2021)
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
The Advantage Regret-Matching Actor-Critic. CoRR (2020)
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
Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients. AAMAS (2020)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. NeurIPS (2020)
Dustin Morrill, Ryan D'Orazio, Reca Sarfati, Marc Lanctot, James R. Wright, Amy Greenwald, Michael Bowling
Hindsight and Sequential Rationality of Correlated Play. CoRR (2020)
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
Fast computation of Nash Equilibria in Imperfect Information Games. ICML (2020)
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
A Generalized Training Approach for Multiagent Learning. ICLR (2020)
Michal Sustr, Martin Schmid, Matej Moravcík, Neil Burch, Marc Lanctot, Michael Bowling
Sound Search in Imperfect Information Games. CoRR (2020)
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling
The Hanabi challenge: A new frontier for AI research. Artif. Intell. 280 (2020)
Finbarr Timbers, Edward Lockhart, Martin Schmid, Marc Lanctot, Michael Bowling
Approximate exploitability: Learning a best response in large games. CoRR (2020)
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
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR (2020)
Thomas W. Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian M. Gemp, Thomas C. Hudson, Nicolas Porcel, Marc Lanctot, Julien Pérolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach
Learning to Play No-Press Diplomacy with Best Response Policy Iteration. CoRR (2020)
Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel
Negotiating Team Formation Using Deep Reinforcement Learning. CoRR (2020)
Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Z. Leibo, Marc Lanctot, Michael Johanson, Wojciech M. Czarnecki, Thore Graepel
Negotiating team formation using deep reinforcement learning. Artif. Intell. 288 (2020)
Karl Tuyls, Julien Pérolat, Marc Lanctot, Edward Hughes, Richard Everett, Joel Z. Leibo, Csaba Szepesvári, Thore Graepel
Bounds and dynamics for empirical game theoretic analysis. Auton. Agents Multi Agent Syst. 34 (1) (2020)
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling
The Hanabi Challenge: A New Frontier for AI Research. CoRR (2019)
Shayegan Omidshafiei, Christos H. Papadimitriou, Georgios Piliouras, Karl Tuyls, Mark Rowland, Jean-Baptiste Lespiau, Wojciech M. Czarnecki, Marc Lanctot, Julien Pérolat, Rémi Munos
α-Rank: Multi-Agent Evaluation by Evolution. CoRR (2019)
Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines. AAAI (2019)
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
Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence. AI Mag. 40 (3) (2019)
Edward Lockhart, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr Timbers, Karl Tuyls
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent. CoRR (2019)
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
OpenSpiel: A Framework for Reinforcement Learning in Games. CoRR (2019)
Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research. CoRR (2019)
Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Rémi Munos, Julien Pérolat, Marc Lanctot, Audrunas Gruslys, Jean-Baptiste Lespiau, Karl Tuyls
Neural Replicator Dynamics. CoRR (2019)
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
A Generalized Training Approach for Multiagent Learning. CoRR (2019)
Edward Lockhart, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr Timbers, Karl Tuyls
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent. IJCAI (2019)
Sriram Srinivasan, Marc Lanctot, Vinícius Flores Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, Michael Bowling
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. NeurIPS (2018)
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. AAMAS (2018)
Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel
A Generalised Method for Empirical Game Theoretic Analysis. AAMAS (2018)
Sriram Srinivasan, Marc Lanctot, Vinícius Flores Zambaldi, Julien Pérolat, Karl Tuyls, Rémi Munos, Michael Bowling
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments. CoRR (2018)
Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines. CoRR (2018)
Karl Tuyls, Julien Pérolat, Marc Lanctot, Joel Z. Leibo, Thore Graepel
A Generalised Method for Empirical Game Theoretic Analysis. CoRR (2018)
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
Deep Q-learning From Demonstrations. AAAI (2018)
Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z. Leibo, Karl Tuyls, Stephen Clark
Emergent Communication through Negotiation. ICLR (Poster) (2018)
Kris Cao, Angeliki Lazaridou, Marc Lanctot, Joel Z. Leibo, Karl Tuyls, Stephen Clark
Emergent Communication through Negotiation. CoRR (2018)
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy P. Lillicrap, Karen Simonyan, Demis Hassabis
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. CoRR (2017)
Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel
Multi-agent Reinforcement Learning in Sequential Social Dilemmas. AAMAS (2017)
Todd Hester, Matej Vecerík, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John P. Agapiou, Joel Z. Leibo, Audrunas Gruslys
Learning from Demonstrations for Real World Reinforcement Learning. CoRR (2017)
Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. CoRR (2017)
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel
Value-Decomposition Networks For Cooperative Multi-Agent Learning. CoRR (2017)
Karl Tuyls, Julien Pérolat, Marc Lanctot, Georg Ostrovski, Rahul Savani, Joel Z. Leibo, Toby Ord, Thore Graepel, Shane Legg
Symmetric Decomposition of Asymmetric Games. CoRR (2017)
Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, Thore Graepel
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning. NIPS (2017)
Joel Z. Leibo, Vinícius Flores Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel
Multi-agent Reinforcement Learning in Sequential Social Dilemmas. CoRR (2017)
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis
Mastering the game of Go with deep neural networks and tree search. Nat. 529 (7587) (2016)
Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves
Memory-Efficient Backpropagation Through Time. CoRR (2016)
Branislav Bosanský, Viliam Lisý, Marc Lanctot, Jirí Cermák, Mark H. M. Winands
Algorithms for computing strategies in two-player simultaneous move games. Artif. Intell. 237 (2016)
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
Dueling Network Architectures for Deep Reinforcement Learning. ICML (2016)
Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
Convolution by Evolution: Differentiable Pattern Producing Networks. GECCO (2016)
Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, Alex Graves
Memory-Efficient Backpropagation Through Time. NIPS (2016)
Chrisantha Fernando, Dylan Banarse, Malcolm Reynolds, Frederic Besse, David Pfau, Max Jaderberg, Marc Lanctot, Daan Wierstra
Convolution by Evolution: Differentiable Pattern Producing Networks. CoRR (2016)
Johannes Heinrich, Marc Lanctot, David Silver
Fictitious Self-Play in Extensive-Form Games. ICML (2015)
Viliam Lisý, Marc Lanctot, Michael H. Bowling
Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games. AAMAS (2015)
Ziyu Wang, Nando de Freitas, Marc Lanctot
Dueling Network Architectures for Deep Reinforcement Learning. CoRR (2015)
Mandy J. W. Tak, Marc Lanctot, Mark H. M. Winands
Monte Carlo Tree Search variants for simultaneous move games. CIG (2014)
Tom Pepels, Mark H. M. Winands, Marc Lanctot
Real-Time Monte Carlo Tree Search in Ms Pac-Man. IEEE Trans. Comput. Intell. AI Games 6 (3) (2014)
Marc Lanctot
Further developments of extensive-form replicator dynamics using the sequence-form representation. AAMAS (2014)
Tom Pepels, Mandy J. W. Tak, Marc Lanctot, Mark H. M. Winands
Quality-based Rewards for Monte-Carlo Tree Search Simulations. ECAI (2014)
Marc J. V. Ponsen, Steven de Jong, Marc Lanctot
Computing Approximate Nash Equilibria and Robust Best-Responses Using Sampling. CoRR (2014)
Marc Lanctot, Mark H. M. Winands, Tom Pepels, Nathan R. Sturtevant
Monte Carlo Tree Search with heuristic evaluations using implicit minimax backups. CIG (2014)
Tom Pepels, Tristan Cazenave, Mark H. M. Winands, Marc Lanctot
Minimizing Simple and Cumulative Regret in Monte-Carlo Tree Search. CGW@ECAI (2014)
Marc Lanctot, Mark H. M. Winands, Tom Pepels, Nathan R. Sturtevant
Monte Carlo Tree Search with Heuristic Evaluations using Implicit Minimax Backups. CoRR (2014)
Markus Esser, Michael Gras, Mark H. M. Winands, Maarten P. D. Schadd, Marc Lanctot
Improving Best-Reply Search. Computers and Games (2013)
Viliam Lisý, Vojtech Kovarík, Marc Lanctot, Branislav Bosanský
Convergence of Monte Carlo Tree Search in Simultaneous Move Games. CoRR (2013)

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Publication Activity (10 Years)

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Marc Lanctot