julien perolat
julien perolat
Verified email at google.com
Cited by
Cited by
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, 4190-4203, 2017
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, 3643-3652, 2017
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
Re-evaluating evaluation
D Balduzzi, K Tuyls, J Perolat, T Graepel
Advances in Neural Information Processing Systems, 3268-3279, 2018
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
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
α-rank: Multi-agent evaluation by evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
Scientific reports 9 (1), 1-29, 2019
Open-ended learning in symmetric zero-sum games
D Balduzzi, M Garnelo, Y Bachrach, WM Czarnecki, J Perolat, ...
arXiv preprint arXiv:1901.08106, 2019
A generalised method for empirical game theoretic analysis
K Tuyls, J Perolat, M Lanctot, JZ Leibo, T Graepel
arXiv preprint arXiv:1803.06376, 2018
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
Human-machine dialogue as a stochastic game
M Barlier, J Perolat, R Laroche, O Pietquin
Proceedings of the 16th Annual Meeting of the Special Interest Group on …, 2015
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
Actor-critic fictitious play in simultaneous move multistage games
J Perolat, B Piot, O Pietquin
Symmetric decomposition of asymmetric games
K Tuyls, J Perolat, M Lanctot, G Ostrovski, R Savani, JZ Leibo, T Ord, ...
Scientific Reports 8 (1), 1-20, 2018
Softened approximate policy iteration for Markov games
J Pérolat, B Piot, M Geist, B Scherrer, O Pietquin
ICML 2016-33rd International Conference on Machine Learning, 2016
Multiagent evaluation under incomplete information
M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ...
Advances in Neural Information Processing Systems, 12291-12303, 2019
Malthusian reinforcement learning
JZ Leibo, J Perolat, E Hughes, S Wheelwright, AH Marblestone, ...
arXiv preprint arXiv:1812.07019, 2018
Approximate fictitious play for mean field games
R Elie, J Pérolat, M Laurière, M Geist, O Pietquin
arXiv preprint arXiv:1907.02633, 2019
Playing the game of universal adversarial perturbations
J Perolat, M Malinowski, B Piot, O Pietquin
arXiv preprint arXiv:1809.07802, 2018
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
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