Sledovat
Marc Lanctot
Marc Lanctot
Research Scientist, Google DeepMind
E-mailová adresa ověřena na: google.com - Domovská stránka
Název
Citace
Citace
Rok
Mastering the game of Go with deep neural networks and tree search
D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ...
Nature 529 (7587), 484-489, 2016
196962016
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ...
Science 362 (6419), 1140-1144, 2018
6739*2018
Dueling Network Architectures for Deep Reinforcement Learning
Z Wang, T Schaul, M Hessel, H van Hasselt, M Lanctot, N de Freitas
arXiv preprint arXiv:1511.06581, 2016
50962016
Value-decomposition networks for cooperative multi-agent learning based on team reward
P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ...
Proceedings of the 17th international conference on autonomous agents and …, 2018
1810*2018
Deep Q-learning from Demonstrations
T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ...
Association for the Advancement of Artificial Intelligence (AAAI), 2018
12612018
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel
AAMAS, 2017
8992017
A unified game-theoretic approach to multiagent reinforcement learning
M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ...
arXiv preprint arXiv:1711.00832, 2017
7422017
The hanabi challenge: A new frontier for ai research
N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ...
Artificial Intelligence 280, 103216, 2020
4202020
Fictitious Self-Play in Extensive-Form Games
J Heinrich, M Lanctot, D Silver
International Conference on Machine Learning, 2015
3862015
Monte Carlo sampling for regret minimization in extensive games
M Lanctot, K Waugh, M Zinkevich, M Bowling
Advances in neural information processing systems 22, 1078-1086, 2009
3822009
OpenSpiel: A Framework for Reinforcement Learning in Games
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
2692019
Memory-efficient backpropagation through time
A Gruslys, R Munos, I Danihelka, M Lanctot, A Graves
Advances In Neural Information Processing Systems, 4125-4133, 2016
259*2016
Mastering the game of Stratego with model-free multiagent reinforcement learning
J Perolat, B De Vylder, D Hennes, E Tarassov, F Strub, V de Boer, ...
Science 378 (6623), 990-996, 2022
1952022
Emergent Communication through Negotiation
K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark
arXiv preprint arXiv:1804.03980, 2018
1882018
Actor-critic policy optimization in partially observable multiagent environments
S Srinivasan, M Lanctot, V Zambaldi, J Pérolat, K Tuyls, R Munos, ...
Advances in Neural Information Processing Systems, 3422-3435, 2018
1642018
α-Rank: Multi-Agent Evaluation by Evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
Scientific reports 9 (1), 9937, 2019
1382019
Convolution by evolution: Differentiable pattern producing networks
C Fernando, D Banarse, M Reynolds, F Besse, D Pfau, M Jaderberg, ...
Proceedings of the Genetic and Evolutionary Computation Conference 2016, 109-116, 2016
1362016
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
JZ Leibo, E Hughes, M Lanctot, T Graepel
arXiv preprint arXiv:1903.00742, 2019
1242019
Real-Time Monte-Carlo Tree Search in Ms Pac-Man
T Pepels, MHM Winands, M Lanctot
Transactions on Computation Intelligence and AI in Games, 2014
1162014
Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization.
M Johanson, N Bard, M Lanctot, RG Gibson, M Bowling
AAMAS, 837-846, 2012
1122012
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Články 1–20