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julien perolat
julien perolat
DeepMind
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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
5092017
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
1712017
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
1492019
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
1352018
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
1092019
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
942015
α-Rank: Multi-Agent Evaluation by Evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
Scientific reports 9 (1), 9937, 2019
902019
Re-evaluating evaluation
D Balduzzi, K Tuyls, J Perolat, T Graepel
Advances in Neural Information Processing Systems 31, 2018
872018
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
632015
A generalised method for empirical game theoretic analysis
K Tuyls, J Perolat, M Lanctot, JZ Leibo, T Graepel
arXiv preprint arXiv:1803.06376, 2018
612018
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
602019
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
602019
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
592018
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
582020
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
542020
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
482021
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
442017
Symmetric decomposition of asymmetric games
K Tuyls, J Pérolat, M Lanctot, G Ostrovski, R Savani, JZ Leibo, T Ord, ...
Scientific reports 8 (1), 1-20, 2018
382018
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
362021
Neural replicator dynamics: Multiagent learning via hedging policy gradients
D Hennes, D Morrill, S Omidshafiei, R Munos, J Perolat, M Lanctot, ...
Proceedings of the 19th International Conference on Autonomous Agents and …, 2020
34*2020
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Artikelen 1–20