| 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 | 727 | 2017 |
| 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 | 252 | 2019 |
| 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 | 214 | 2017 |
| 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 | 176 | 2019 |
| 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 | 160 | 2018 |
| 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 | 158 | 2022 |
| α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019 | 130 | 2019 |
| 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 | 117 | 2020 |
| 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 | 117 | 2015 |
| Re-evaluating evaluation D Balduzzi, K Tuyls, J Perolat, T Graepel Advances in Neural Information Processing Systems 31, 2018 | 110 | 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 | 104 | 2019 |
| 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 | 92 | 2021 |
| 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 | 92 | 2020 |
| 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 | 81 | 2015 |
| 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 | 80 | 2021 |
| 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 | 78 | 2018 |
| 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 | 75 | 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 | 74 | 2018 |
| 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 | 64 | 2021 |
| Concave utility reinforcement learning: The mean-field game viewpoint M Geist, J Pérolat, M Laurière, R Elie, S Perrin, O Bachem, R Munos, ... arXiv preprint arXiv:2106.03787, 2021 | 63 | 2021 |
Marc LanctotResearch Scientist, Google DeepMind在 google.com 的电子邮件经过验证
