| 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 | 148 | 2019 |
| A Generalized Training Approach for Multiagent Learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... ICLR2020, 2019 | 60 | 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 | 35 | 2021 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SM Eslami, D Hennes, WM Czarnecki, ... arXiv preprint arXiv:2105.12196, 2021 | 30 | 2021 |
| Navigating the landscape of multiplayer games S Omidshafiei, K Tuyls, WM Czarnecki, FC Santos, M Rowland, J Connor, ... Nature communications 11 (1), 5603, 2020 | 25 | 2020 |
| Multi-agent training beyond zero-sum with correlated equilibrium meta-solvers L Marris, P Muller, M Lanctot, K Tuyls, T Graepel International Conference on Machine Learning, 7480-7491, 2021 | 13 | 2021 |
| Scalable deep reinforcement learning algorithms for mean field games M Laurière, S Perrin, S Girgin, P Muller, A Jain, T Cabannes, G Piliouras, ... International Conference on Machine Learning, 12078-12095, 2022 | 10 | 2022 |
| 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 | 8 | 2022 |
| Learning Equilibria in Mean-Field Games: Introducing Mean-Field PSRO P Muller, M Rowland, R Elie, G Piliouras, J Perolat, M Lauriere, R Marinier, ... arXiv preprint arXiv:2111.08350, 2021 | 5 | 2021 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ... Science Robotics 7 (69), eabo0235, 2022 | 3 | 2022 |
| Learning Correlated Equilibria in Mean-Field Games P Muller, R Elie, M Rowland, M Lauriere, J Perolat, S Perrin, M Geist, ... arXiv preprint arXiv:2208.10138, 2022 | 3 | 2022 |
| Multiagent off-screen behavior prediction in football S Omidshafiei, D Hennes, M Garnelo, Z Wang, A Recasens, E Tarassov, ... Scientific reports 12 (1), 8638, 2022 | 2 | 2022 |
| Controller optimization via reinforcement learning on asset avatar Z Li, P Muller, P Nirgudkar US Patent App. 16/878,692, 2020 | 2 | 2020 |
| Developing, evaluating and scaling learning agents in multi-agent environments I Gemp, T Anthony, Y Bachrach, A Bhoopchand, K Bullard, J Connor, ... AI Communications, 1-14, 2022 | 1 | 2022 |
| Time-series imputation of temporally-occluded multiagent trajectories S Omidshafiei, D Hennes, M Garnelo, E Tarassov, Z Wang, R Elie, ... arXiv preprint arXiv:2106.04219, 2021 | 1 | 2021 |
| Temporal difference and return optimism in cooperative multi-agent reinforcement learning M Rowland, S Omidshafiei, D Hennes, W Dabney, A Jaegle, P Muller, ... AAMAS ALA Workshop, 2021 | 1 | 2021 |
| Automated offset well analysis C Jeong, FJ Gomez, M Ringer, P Bolchover, P Muller US Patent 11,143,775, 2021 | | 2021 |
Karl TuylsResearch Scientist, Google DeepMind and Professor of computer science, University of LiverpoolVerified email at google.com
