Marc Lanctot

发表

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

Demis Hassabis, Karen Simonyan, David Silver, 2018, Science.

Mark H. M. Winands, Marc Lanctot, 2013, J. Int. Comput. Games Assoc..

Negotiating team formation using deep reinforcement learning

Joel Z. Leibo, Thore Graepel, Yoram Bachrach, 2020, Artif. Intell..

Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines pdf

Michael H. Bowling, Rudolf Kadlec, Martin Schmid, 2018, AAAI.

Fictitious Self-Play in Extensive-Form Games

David Silver, Marc Lanctot, Johannes Heinrich, 2015, ICML.

SIA Wins Surakarta Tournament

Mark H. M. Winands, Marc Lanctot, 2013, J. Int. Comput. Games Assoc..

Algorithms for computing strategies in two-player simultaneous move games

Branislav Bosanský, Mark H. M. Winands, Viliam Lisý, 2016, Artif. Intell..

Deep Q-learning From Demonstrations

Tom Schaul, Andrew Sendonaris, Joel Z. Leibo, 2017, AAAI.

α-Rank: Multi-Agent Evaluation by Evolution

Christos H. Papadimitriou, Rémi Munos, Wojciech Czarnecki, 2019, Scientific Reports.

Variance Reduction in Monte-Carlo Tree Search

Joel Veness, Michael H. Bowling, Marc Lanctot, 2011, NIPS.

Approximate exploitability: Learning a best response in large games pdf

Michael Bowling, Marc Lanctot, Martin Schmid, 2020, ArXiv.

Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent

Karl Tuyls, Marc Lanctot, Dustin Morrill, 2019, IJCAI.

The Hanabi Challenge: A New Frontier for AI Research

H. Francis Song, Hugo Larochelle, Marc G. Bellemare, 2019, Artif. Intell..

Symmetric Decomposition of Asymmetric Games

Shane Legg, Rahul Savani, Joel Z. Leibo, 2017, Scientific Reports.

Locally-Adaptive Virtual Environments in Persistent-state Multi-player Games

Clark Verbrugge, Marc Lanctot, 2004 .

Hindsight and Sequential Rationality of Correlated Play pdf

Michael Bowling, Amy Greenwald, Marc Lanctot, 2020, ArXiv.

Bounds and dynamics for empirical game theoretic analysis

Csaba Szepesvári, Joel Z. Leibo, Thore Graepel, 2019, Autonomous Agents and Multi-Agent Systems.

Memory-Efficient Backpropagation Through Time

Alex Graves, Rémi Munos, Marc Lanctot, 2016, NIPS.

Adversarial Planning Through Strategy Simulation

Michael Buro, Marc Lanctot, Franisek Sailer, 2007, 2007 IEEE Symposium on Computational Intelligence and Games.

MCRNR: Fast Computing of Restricted Nash Responses by Means of Sampling

Marc Lanctot, Steven de Jong, Marc J. V. Ponsen, 2010, Interactive Decision Theory and Game Theory.

A Generalised Method for Empirical Game Theoretic Analysis

Joel Z. Leibo, Thore Graepel, Karl Tuyls, 2018, AAMAS.

The Advantage Regret-Matching Actor-Critic pdf

Michael H. Bowling, Michael Bowling, Marc Lanctot, 2020, ArXiv.

Solving Common-Payoff Games with Approximate Policy Iteration pdf

Michael H. Bowling, Michael Bowling, Elnaz Davoodi, 2021, AAAI.

An Introduction to Counterfactual Regret Minimization

Todd W. Neller, Marc Lanctot, Marc Lanctot, 2013 .

Convolution by Evolution: Differentiable Pattern Producing Networks

David Pfau, Max Jaderberg, Chrisantha Fernando, 2016, GECCO.

From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization pdf

Georgios Piliouras, Neil Burch, David Balduzzi, 2020, ICML.

Monte Carlo Tree Search with heuristic evaluations using implicit minimax backups

Nathan R. Sturtevant, Mark H. M. Winands, Marc Lanctot, 2014, 2014 IEEE Conference on Computational Intelligence and Games.

Sound Search in Imperfect Information Games pdf

Michael H. Bowling, Neil Burch, Marc Lanctot, 2020, ArXiv.

Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions

Duane Szafron, Neil Burch, Marc Lanctot, 2012, NIPS.

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

Guy Lever, Joel Z. Leibo, Max Jaderberg, 2018, AAMAS.

Further developments of extensive-form replicator dynamics using the sequence-form representation

Marc Lanctot, Marc Lanctot, 2014, AAMAS.

Actor-Critic Policy Optimization in Partially Observable Multiagent Environments pdf

Michael H. Bowling, Rémi Munos, Karl Tuyls, 2018, NeurIPS.

Monte Carlo Tree Search variants for simultaneous move games

Mark H. M. Winands, Marc Lanctot, Mandy J. W. Tak, 2014, 2014 IEEE Conference on Computational Intelligence and Games.

Adaptive virtual environments in modern multi-player computer games

Marc Lanctot, Marc Lanctot, 2005 .

Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients

Daniel Hennes, Marc Lanctot, Karl Tuyls, 2020, AAMAS.

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

Mark H. M. Winands, Marc Lanctot, Tom Pepels, 2014, IEEE Transactions on Computational Intelligence and AI in Games.

Fast computation of Nash Equilibria in Imperfect Information Games

Daniel Hennes, Marc Lanctot, Karl Tuyls, 2020, ICML.

Improving Best-Reply Search

Mark H. M. Winands, Marc Lanctot, Maarten P. D. Schadd, 2013, Computers and Games.

Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization

Michael H. Bowling, Marc Lanctot, Nolan Bard, 2012, AAMAS.

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

Demis Hassabis, Karen Simonyan, David Silver, 2017, ArXiv.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

Julian Togelius, Quanshi Zhang, Christopher W. Geib, 2019, AI Mag..

Multi-agent Reinforcement Learning in Sequential Social Dilemmas pdf

Joel Z. Leibo, Thore Graepel, Marc Lanctot, 2017, AAMAS.

Monte Carlo Sampling for Regret Minimization in Extensive Games

Kevin Waugh, Michael H. Bowling, Martin Zinkevich, 2009, NIPS.

OpenSpiel: A Framework for Reinforcement Learning in Games pdf

Sriram Srinivasan, Matthew Lai, Karl Tuyls, 2019, ArXiv.

Dueling Network Architectures for Deep Reinforcement Learning pdf

Tom Schaul, Nando de Freitas, Matteo Hessel, 2015, ICML.

Search in Imperfect Information Games Using Online Monte Carlo Counterfactual Regret Minimization

Michael Bowling, Marc Lanctot, Viliam Lisy, 2014, AAAI 2014.

Neural Replicator Dynamics pdf

Rémi Munos, Karl Tuyls, Daniel Hennes, 2019, ArXiv.

Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games

Michael H. Bowling, Viliam Lisý, Marc Lanctot, 2015, AAMAS.

A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

David Silver, Thore Graepel, Karl Tuyls, 2017, NIPS.

No-Regret Learning in Extensive-Form Games with Imperfect Recall pdf

Michael H. Bowling, Neil Burch, Marc Lanctot, 2012, ICML.

Minimizing Simple and Cumulative Regret in Monte-Carlo Tree Search

Mark H. M. Winands, Tristan Cazenave, Marc Lanctot, 2014, CGW@ECAI.

Learning to Play No-Press Diplomacy with Best Response Policy Iteration pdf

Tom Eccles, Satinder Singh, Julien P'erolat, 2020, NeurIPS.

Monte Carlo *-Minimax Search (abstract)

Christopher Archibald, Joel Veness, Abdallah Saffidine, 2013 .

Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel

Mark H. M. Winands, Viliam Lisý, Marc Lanctot, 2013, CGW@IJCAI.

Quality-based Rewards for Monte-Carlo Tree Search Simulations

Mark H. M. Winands, Marc Lanctot, Tom Pepels, 2014, ECAI.

Convergence of Monte Carlo Tree Search in Simultaneous Move Games

Branislav Bosanský, Viliam Lisý, Marc Lanctot, 2013, NIPS.

alpha-Rank: Multi-Agent Evaluation by Evolution

Karl Tuyls, Georgios Piliouras, Marc Lanctot, 2019 .

Generalized Sampling and Variance in Counterfactual Regret Minimization

Duane Szafron, Michael H. Bowling, Neil Burch, 2012, AAAI.

Learning from Demonstrations for Real World Reinforcement Learning pdf

Tom Schaul, Andrew Sendonaris, Joel Z. Leibo, 2017, ArXiv.

Monte Carlo Tree Search for Simultaneous Move Games: A Case Study in the Game of Tron

Mark H. M. Winands, Marc Lanctot, Christopher Wittlinger, 2013 .

Emergent Communication through Negotiation pdf

Stephen Clark, Joel Z. Leibo, Karl Tuyls, 2018, ICLR.

Monte Carlo *-Minimax Search pdf

Christopher Archibald, Joel Veness, Mark H. M. Winands, 2013, IJCAI.

Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research pdf

Joel Z. Leibo, Thore Graepel, Marc Lanctot, 2019, ArXiv.

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

Demis Hassabis, Koray Kavukcuoglu, David Silver, 2016, Nature.

Computing Approximate Nash Equilibria and Robust Best-Responses Using Sampling pdf

Marc Lanctot, Steven de Jong, Marc J. V. Ponsen, 2011, J. Artif. Intell. Res..

A Generalized Training Approach for Multiagent Learning pdf

Guy Lever, Thore Graepel, Karl Tuyls, 2020, ICLR.

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

Angeliki Lazaridou, Marc Lanctot, Abhinav Gupta, 2021, ArXiv.

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

Michael Bowling, Amy Greenwald, James R. Wright, 2021, ICML.

Hindsight and Sequential Rationality of Correlated Play

Michael Bowling, Amy Greenwald, Marc Lanctot, 2021, AAAI.

Player of Games pdf

Kevin Waugh, Michael H. Bowling, Rudolf Kadlec, 2021, ArXiv.

Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent pdf

Tom Eccles, Rahul Savani, Yoram Bachrach, 2021, ArXiv.

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

Thore Graepel, Karl Tuyls, Marc Lanctot, 2021, ICML.

Anytime Optimal PSRO for Two-Player Zero-Sum Games pdf

Pierre Baldi, Roy Fox, Marc Lanctot, 2022 .

PATH-FINDING FOR LARGE SCALE MULTIPLAYER COMPUTER GAMES

Marc Lanctot, Marc Lanctot, N. Ng, 2006 .

The Second Annual Real-Time Strategy Game AI Competition

Michael Buro, Marc Lanctot, Sterling Orsten, 2007 .