julien perolat
julien perolat
Verified email at google.com
TitleCited byYear
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
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
Re-evaluating evaluation
D Balduzzi, K Tuyls, J Perolat, T Graepel
Advances in Neural Information Processing Systems, 3268-3279, 2018
Approximate dynamic programming for two-player zero-sum markov games
B Scherrer
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
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
A generalised method for empirical game theoretic analysis
K Tuyls, J Perolat, M Lanctot, JZ Leibo, T Graepel
Proceedings of the 17th International Conference on Autonomous Agents and …, 2018
Symmetric decomposition of asymmetric games
K Tuyls, J Pérolat, M Lanctot, G Ostrovski, R Savani, JZ Leibo, T Ord, ...
Scientific reports 8 (1), 1015, 2018
Softened approximate policy iteration for Markov games
J Pérolat, B Piot, M Geist, B Scherrer, O Pietquin
Open-ended learning in symmetric zero-sum games
D Balduzzi, M Garnelo, Y Bachrach, WM Czarnecki, J Perolat, ...
arXiv preprint arXiv:1901.08106, 2019
On the Use of Non-Stationary Strategies for Solving Two-Player Zero-Sum Markov Games.
J Pérolat, B Piot, B Scherrer, O Pietquin
AISTATS, 893-901, 2016
Actor-critic fictitious play in simultaneous move multistage games
J Perolat, B Piot, O Pietquin
{\alpha}-Rank: Multi-Agent Evaluation by Evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
arXiv preprint arXiv:1903.01373, 2019
Playing the game of universal adversarial perturbations
J Perolat, M Malinowski, B Piot, O Pietquin
arXiv preprint arXiv:1809.07802, 2018
Learning nash equilibrium for general-sum markov games from batch data
J Pérolat, F Strub, B Piot, O Pietquin
arXiv preprint arXiv:1606.08718, 2016
Malthusian reinforcement learning
JZ Leibo, J Perolat, E Hughes, S Wheelwright, AH Marblestone, ...
Proceedings of the 18th International Conference on Autonomous Agents and …, 2019
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
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
Approximate Fictitious Play for Mean Field Games
R Elie, J Pérolat, M Laurière, M Geist, O Pietquin
arXiv preprint arXiv:1907.02633, 2019
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