| 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 | 267 | 2017 |
| 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 | 96 | 2017 |
| 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 | 72 | 2018 |
| Re-evaluating evaluation D Balduzzi, K Tuyls, J Perolat, T Graepel arXiv preprint arXiv:1806.02643, 2018 | 51 | 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 | 48 | 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 | 45 | 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 | 40 | 2019 |
| 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 | 39 | 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 | 38 | 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 | 36 | 2018 |
| 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 | 28 | 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 | 25 | 2019 |
| 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 | 23 | 2018 |
| 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 | 21 | 2018 |
| 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 | 20 | 2016 |
| Malthusian reinforcement learning JZ Leibo, J Perolat, E Hughes, S Wheelwright, AH Marblestone, ... arXiv preprint arXiv:1812.07019, 2018 | 18 | 2018 |
| Multiagent evaluation under incomplete information M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ... arXiv preprint arXiv:1909.09849, 2019 | 17 | 2019 |
| 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 | 14 | 2019 |
| Approximate fictitious play for mean field games R Elie, J Pérolat, M Laurière, M Geist, O Pietquin | 14 | 2019 |
| 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 | 14 | 2017 |
Karl TuylsResearch Scientist, Google DeepMind and Professor of computer science, University of LiverpoolVerified email at google.com
