Marc Lanctot

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Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCLPatvirtintas el. paštas ucl.ac.uk
Karl TuylsResearch Scientist, Entrepreneur, ex-Google DeepMind, Prof at University of LiverpoolPatvirtintas el. paštas hcompany.ai
David SilverDeepMind, UCLPatvirtintas el. paštas google.com
Michael BowlingAmii, University of AlbertaPatvirtintas el. paštas ualberta.ca
Julian SchrittwieserDeepMindPatvirtintas el. paštas furidamu.org
Laurent SifreGoogle DeepMindPatvirtintas el. paštas polytechnique.edu
Arthur GuezGoogle DeepMindPatvirtintas el. paštas google.com
Joel Z LeiboResearch scientistPatvirtintas el. paštas google.com
julien perolatDeepMindPatvirtintas el. paštas google.com
Timothy P. LillicrapDirector of Research, Google DeepMindPatvirtintas el. paštas google.com
Audrūnas GruslysPatvirtintas el. paštas gruslys.com
Chris J. MaddisonUniversity of TorontoPatvirtintas el. paštas cs.toronto.edu
George van den DriesscheDeepMindPatvirtintas el. paštas deepmind.com
Neil BurchSony AI & Alberta Machine Intelligence Institute, University of AlbertaPatvirtintas el. paštas ualberta.ca
Vinicius ZambaldiGoogle DeepmindPatvirtintas el. paštas google.com
Thomas HubertGoogle DeepmindPatvirtintas el. paštas google.com
Rémi MunosGoogle DeepMindPatvirtintas el. paštas inria.fr
Nal KalchbrennerGoogle DeepMindPatvirtintas el. paštas google.com
koray kavukcuogluDeepMindPatvirtintas el. paštas kavukcuoglu.org
Ilya SutskeverCo-Founder and Chief Scientist of OpenAIPatvirtintas el. paštas openai.com

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Marc Lanctot

Research Scientist, Google DeepMind

Patvirtintas el. paštas google.com - Pagrindinis puslapis

Artificial Intelligence Game Theory Search Multiagent Systems Reinforcement Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	19932	2016
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	6873*	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	5176	2016
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	1850*	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	1281	2018
Multi-agent Reinforcement Learning in Sequential Social Dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel AAMAS, 2017	904	2017
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	753	2017
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	424	2020
Fictitious Self-Play in Extensive-Form Games J Heinrich, M Lanctot, D Silver International Conference on Machine Learning, 2015	397	2015
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	385	2009
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	271	2019
Memory-efficient backpropagation through time A Gruslys, R Munos, I Danihelka, M Lanctot, A Graves Advances In Neural Information Processing Systems, 4125-4133, 2016	261*	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	200	2022
Emergent Communication through Negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018	189	2018
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	165	2018
α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019	139	2019
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	136	2016
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	124	2019
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	116	2014
A Generalized Training Approach for Multiagent Learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... arXiv preprint arXiv:1909.12823, 2019	113	2019

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