Следене
julien perolat
julien perolat
DeepMind
Потвърден имейл адрес: google.com
Заглавие
Позовавания
Позовавания
Година
A unified game-theoretic approach to multiagent reinforcement learning
M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ...
Advances in neural information processing systems 30, 2017
7062017
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
2292019
A multi-agent reinforcement learning model of common-pool resource appropriation
J Perolat, JZ Leibo, V Zambaldi, C Beattie, K Tuyls, T Graepel
Advances in neural information processing systems 30, 2017
2092017
Open-ended learning in symmetric zero-sum games
D Balduzzi, M Garnelo, Y Bachrach, W Czarnecki, J Perolat, M Jaderberg, ...
International Conference on Machine Learning, 434-443, 2019
1712019
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 31, 2018
1582018
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
1402022
α-Rank: Multi-Agent Evaluation by Evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
Scientific reports 9 (1), 9937, 2019
1202019
Approximate dynamic programming for two-player zero-sum markov games
J Perolat, B Scherrer, B Piot, O Pietquin
International Conference on Machine Learning, 1321-1329, 2015
1132015
Fictitious play for mean field games: Continuous time analysis and applications
S Perrin, J Pérolat, M Laurière, M Geist, R Elie, O Pietquin
Advances in neural information processing systems 33, 13199-13213, 2020
1052020
Re-evaluating evaluation
D Balduzzi, K Tuyls, J Perolat, T Graepel
Advances in Neural Information Processing Systems 31, 2018
1052018
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
992019
On the convergence of model free learning in mean field games
R Elie, J Perolat, M Laurière, M Geist, O Pietquin
Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 7143-7150, 2020
832020
Game Plan: What AI can do for Football, and What Football can do for AI
K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ...
Journal of Artificial Intelligence Research 71, 41-88, 2021
822021
Generalizing the Wilcoxon rank-sum test for interval data
J Perolat, I Couso, K Loquin, O Strauss
International Journal of Approximate Reasoning 56, 108-121, 2015
802015
A generalised method for empirical game theoretic analysis
K Tuyls, J Perolat, M Lanctot, JZ Leibo, T Graepel
arXiv preprint arXiv:1803.06376, 2018
752018
From poincaré recurrence to convergence in imperfect information games: Finding equilibrium via regularization
J Perolat, R Munos, JB Lespiau, S Omidshafiei, M Rowland, P Ortega, ...
International Conference on Machine Learning, 8525-8535, 2021
742021
Actor-critic fictitious play in simultaneous move multistage games
J Perolat, B Piot, O Pietquin
International Conference on Artificial Intelligence and Statistics, 919-928, 2018
742018
Computing approximate equilibria in sequential adversarial games by exploitability descent
E Lockhart, M Lanctot, J Pérolat, JB Lespiau, D Morrill, F Timbers, K Tuyls
arXiv preprint arXiv:1903.05614, 2019
732019
Scaling up mean field games with online mirror descent
J Perolat, S Perrin, R Elie, M Laurière, G Piliouras, M Geist, K Tuyls, ...
arXiv preprint arXiv:2103.00623, 2021
582021
Learning Nash equilibrium for general-sum Markov games from batch data
J Pérolat, F Strub, B Piot, O Pietquin
Artificial intelligence and statistics, 232-241, 2017
572017
Системата не може да изпълни операцията сега. Опитайте отново по-късно.
Статии 1–20