julien perolat
julien perolat
DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
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
2542017
A multi-agent reinforcement learning model of common-pool resource appropriation
J Perolat, JZ Leibo, V Zambaldi, C Beattie, K Tuyls, T Graepel
arXiv preprint arXiv:1707.06600, 2017
892017
Actor-critic policy optimization in partially observable multiagent environments
S Srinivasan, M Lanctot, V Zambaldi, J Pérolat, K Tuyls, R Munos, ...
arXiv preprint arXiv:1810.09026, 2018
702018
Re-evaluating evaluation
D Balduzzi, K Tuyls, J Perolat, T Graepel
arXiv preprint arXiv:1806.02643, 2018
482018
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
482015
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
422015
α-rank: Multi-agent evaluation by evolution
S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ...
Scientific reports 9 (1), 1-29, 2019
382019
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
362019
A generalised method for empirical game theoretic analysis
K Tuyls, J Perolat, M Lanctot, JZ Leibo, T Graepel
arXiv preprint arXiv:1803.06376, 2018
352018
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
322019
Human-machine dialogue as a stochastic game
M Barlier, J Perolat, R Laroche, O Pietquin
16th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2015), 2015
282015
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
232019
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
212018
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
202018
Softened approximate policy iteration for Markov games
J Pérolat, B Piot, M Geist, B Scherrer, O Pietquin
International Conference on Machine Learning, 1860-1868, 2016
182016
Multiagent evaluation under incomplete information
M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ...
arXiv preprint arXiv:1909.09849, 2019
152019
Malthusian reinforcement learning
JZ Leibo, J Perolat, E Hughes, S Wheelwright, AH Marblestone, ...
arXiv preprint arXiv:1812.07019, 2018
142018
Approximate fictitious play for mean field games
R Elie, J Pérolat, M Laurière, M Geist, O Pietquin
132019
Playing the game of universal adversarial perturbations
J Perolat, M Malinowski, B Piot, O Pietquin
arXiv preprint arXiv:1809.07802, 2018
122018
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
122017
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Articles 1–20