World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
34039
World Ranking
8551
National Ranking
517

Overview

Marc Lanctot is affiliated with DeepMind in the United Kingdom. Their research primarily spans the fields of Computer Science and Decision Sciences, with significant contributions in subfields including Artificial Intelligence, Management Science and Operations Research, Economics and Econometrics, Safety Research, and Sociology and Political Science.

Their scholarly work focuses on several main topics such as Reinforcement Learning in Robotics, Artificial Intelligence in Games, Advanced Bandit Algorithms Research, Experimental Behavioral Economics Studies, Sports Analytics and Performance, Game Theory and Applications, and Adversarial Robustness in Machine Learning.

Marc Lanctot has authored numerous publications, many of which appear in prominent venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Zenodo (CERN European Organization for Nuclear Research)
  • Science
  • Artificial Intelligence

Some recent papers authored or coauthored by Lanctot include:

  • Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver, 2023, arXiv (Cornell University)
  • Mastering the game of Stratego with model-free multiagent reinforcement learning, 2022, Science
  • Negotiating team formation using deep reinforcement learning, 2020, Artificial Intelligence
  • Learning to Play No-Press Diplomacy with Best Response Policy Iteration, 2020, arXiv (Cornell University)
  • From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization, 2020, arXiv (Cornell University)

They frequently collaborate with a number of other researchers. Frequent coauthors include:

  • Michael Bowling
  • Neil Burch
  • Karl Tuyls
  • Luke Marris
  • Ian Gemp

Best Publications

  • Mastering the game of Go with deep neural networks and tree search

    David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez

  • A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Dueling network architectures for deep reinforcement learning

    Ziyu Wang;Tom Schaul;Matteo Hessel;Hado Van Hasselt

  • Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Deep Q-learning from Demonstrations

    Todd Hester;Matej Vecerik;Olivier Pietquin;Marc Lanctot

  • Deep Q-learning From Demonstrations.

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • Multi-agent Reinforcement Learning in Sequential Social Dilemmas

    Joel Z. Leibo;Vinicius Zambaldi;Marc Lanctot;Janusz Marecki

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

    Marc Lanctot;Vinícius Flores Zambaldi;Audrunas Gruslys;Angeliki Lazaridou

  • Monte Carlo Sampling for Regret Minimization in Extensive Games

    Marc Lanctot;Kevin Waugh;Martin Zinkevich;Michael Bowling

  • The Hanabi Challenge: A New Frontier for AI Research

    Nolan Bard;Jakob N. Foerster;Sarath Chandar;Neil Burch

  • Mastering the game of Stratego with model-free multiagent reinforcement learning

    Unknown

  • Fictitious Self-Play in Extensive-Form Games

    Johannes Heinrich;Marc Lanctot;David Silver

  • Learning from Demonstrations for Real World Reinforcement Learning

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • OpenSpiel: A Framework for Reinforcement Learning in Games.

    Marc Lanctot;Edward Lockhart;Jean-Baptiste Lespiau;Vinícius Flores Zambaldi

  • Memory-efficient backpropagation through time

    Marc Lanctot;Audrunas Gruslys;Ivo Danihelka;Remi Munos

  • Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

    Sriram Srinivasan;Marc Lanctot;Vinícius Flores Zambaldi;Julien Pérolat

  • Real-Time Monte Carlo Tree Search in Ms Pac-Man

    Tom Pepels;Mark H. M. Winands;Marc Lanctot

  • Convolution by Evolution: Differentiable Pattern Producing Networks

    Chrisantha Fernando;Dylan Banarse;Malcolm Reynolds;Frederic Besse

  • Emergent Communication through Negotiation

    Kris Cao;Angeliki Lazaridou;Marc Lanctot;Joel Z. Leibo

  • Memory-Efficient Backpropagation Through Time

    Audrūnas Gruslys;Remi Munos;Ivo Danihelka;Marc Lanctot

  • alpha-Rank: Multi-Agent Evaluation by Evolution

    Shayegan Omidshafiei;Christos Papadimitriou;Georgios Piliouras;Karl Tuyls

Frequent Co-Authors

Karl Tuyls
Karl Tuyls DeepMind (United Kingdom)
Michael Bowling
Michael Bowling University of Alberta
Thore Graepel
Thore Graepel University College London
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Neil Burch
Neil Burch University of Alberta
David Silver
David Silver DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Yoram Bachrach
Yoram Bachrach DeepMind (United Kingdom)

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