Marc Lanctot

Google (United States) — United States

Computer Science · Physical Sciences

31h-index26.2kcitations123works0.52yr avg
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Research Topics

Artificial Intelligence in Games(59), Reinforcement Learning in Robotics(57), Sports Analytics and Performance(39), Advanced Bandit Algorithms Research(27), Game Theory and Applications(20)

Publications123 total

Code World Models for General Game Playing
arXiv (Cornell University)·2025Open Access
Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling
2025
Re-evaluating Open-ended Evaluation of Large Language Models
arXiv (Cornell University)·2025Open Access
Deviation Ratings: A General, Clone-Invariant Rating Method
arXiv (Cornell University)·2025Open Access
Jackpot! Alignment as a Maximal Lottery
arXiv (Cornell University)·2025Open Access
Mastering Board Games by External and Internal Planning with Language Models
arXiv (Cornell University)·2024· 2 citedOpen Access
Soft Condorcet Optimization for Ranking of General Agents
arXiv (Cornell University)·2024Open Access
Learning in Games with Progressive Hiding
arXiv (Cornell University)·2024Open Access
Learning Not to Regret
Proceedings of the AAAI Conference on Artificial Intelligence·2024· 2 citedOpen Access
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization
arXiv (Cornell University)·2024Open Access
Approximating the Core via Iterative Coalition Sampling
arXiv (Cornell University)·2024Open Access
Steering Language Models with Game-Theoretic Solvers
arXiv (Cornell University)·2024· 2 citedOpen Access
Neural Population Learning beyond Symmetric Zero-sum Games
arXiv (Cornell University)·2024Open Access
Evaluating Agents using Social Choice Theory
arXiv (Cornell University)·2023· 1 citedOpen Access
Student of Games: A unified learning algorithm for both perfect and imperfect information games
Science Advances·2023· 9 citedOpen Access
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
arXiv (Cornell University)·2023· 3 citedOpen Access
Learning not to Regret
arXiv (Cornell University)·2023Open Access
Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling
arXiv (Cornell University)·2023Open Access
Mastering the game of Stratego with model-free multiagent reinforcement learning
Science·2022· 136 cited
Figure Data for the paper "Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning"
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
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Frequent Co-authors

Karl Tuyls(15), Julien Pérolat(14), Thore Graepel(13), Joel Z. Leibo(11), Rémi Munos(9), Audrūnas Gruslys(8), Michael Bowling(8), Vinícius Zambaldi(7), Neil Burch(7), Jean-Baptiste Lespiau(7), Shayegan Omidshafiei(7), Mark H. M. Winands(7), David Silver(6), Edward Hughes(6), Mark Rowland(5), Richard G. Gibson(5), Laurent Sifre(4), Julian Schrittwieser(4), Demis Hassabis(4), Tom Schaul(4)