Marc Lanctot
Marc Lanctot
Research Scientist, Google DeepMind
Bestätigte E-Mail-Adresse bei google.com - Startseite
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Zitiert von
Jahr
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
88482016
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
1781*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
14622016
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
3832018
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel
AAMAS, 2017
3112017
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
2402017
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
219*2018
Monte Carlo sampling for regret minimization in extensive games
M Lanctot, K Waugh, M Zinkevich, M Bowling
Advances in Neural Information Processing Systems, 1078-1086, 2009
2052009
Fictitious Self-Play in Extensive-Form Games
J Heinrich, M Lanctot, D Silver
International Conference on Machine Learning, 2015
1172015
Memory-efficient backpropagation through time
A Gruslys, R Munos, I Danihelka, M Lanctot, A Graves
Advances In Neural Information Processing Systems, 4125-4133, 2016
115*2016
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
882016
Adversarial planning through strategy simulation
F Sailer, M Buro, M Lanctot
2007 IEEE Symposium on Computational Intelligence and Games, 80-87, 2007
882007
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
842014
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
702020
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
702018
Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization.
M Johanson, N Bard, M Lanctot, RG Gibson, M Bowling
AAMAS, 837-846, 2012
702012
No-Regret Learning in Extensive-Form Games with Imperfect Recall
M Lanctot, R Gibson, N Burch, M Zinkevich, M Bowling
International Conference on Machine Learning, 2012
692012
Emergent Communication through Negotiation
K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark
arXiv preprint arXiv:1804.03980, 2018
652018
Computing approximate Nash equilibria and robust best-responses using sampling
M Ponsen, S De Jong, M Lanctot
Journal of Artificial Intelligence Research, 575-605, 2011
442011
Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games
V Lisý, M Lanctot, M Bowling
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015
43*2015
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