| 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 | 304 | 2019 |
| 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 | 257 | 2022 |
| 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 | 128 | 2022 |
| A Generalized Training Approach for Multiagent Learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... ICLR2020, 2019 | 119 | 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 | 112 | 2021 |
| Scalable deep reinforcement learning algorithms for mean field games M Lauriere, S Perrin, S Girgin, P Muller, A Jain, T Cabannes, G Piliouras, ... International conference on machine learning, 12078-12095, 2022 | 61 | 2022 |
| Learning in mean field games: A survey M Laurière, S Perrin, J Pérolat, S Girgin, P Muller, R Élie, M Geist, ... arXiv preprint arXiv:2205.12944, 2022 | 61 | 2022 |
| Navigating the landscape of multiplayer games S Omidshafiei, K Tuyls, WM Czarnecki, FC Santos, M Rowland, J Connor, ... Nature communications 11 (1), 5603, 2020 | 50 | 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 | 45 | 2021 |
| 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 | 27 | 2021 |
| 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 | 26 | 2022 |
| Combining tree-search, generative models, and Nash bargaining concepts in game-theoretic reinforcement learning Z Li, M Lanctot, KR McKee, L Marris, I Gemp, D Hennes, P Muller, ... arXiv preprint arXiv:2302.00797, 2023 | 17 | 2023 |
| 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 | 15 | 2022 |
| Developing, evaluating and scaling learning agents in multi-agent environments I Gemp, T Anthony, Y Bachrach, A Bhoopchand, K Bullard, J Connor, ... AI Communications 35 (4), 271-284, 2022 | 5 | 2022 |
| Temporal difference and return optimism in cooperative multi-agent reinforcement learning M Rowland, S Omidshafiei, D Hennes, W Dabney, A Jaegle, P Muller, ... Workshop on Adaptive Learning Agents (ALA) at AAMAS, 2021 | 5 | 2021 |
| 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 | 4 | 2021 |
| Search-improved game-theoretic multiagent reinforcement learning in general and negotiation games Z Li, M Lanctot, KR McKee, L Marris, I Gemp, D Hennes, K Larson, ... Proceedings of the 2023 International Conference on Autonomous Agents and …, 2023 | 2 | 2023 |
| Jointly updating agent control policies using estimated best responses to current control policies LC Marris, PFM Muller, M Lanctot, TKH Graepel US Patent App. 18/275,881, 2024 | | 2024 |
| Automated offset well analysis C Jeong, FJ Gomez, M Ringer, P Bolchover, P Muller US Patent App. 18/354,017, 2023 | | 2023 |
| Automated offset well analysis C Jeong, FJ Gomez, M Ringer, P Bolchover, P Muller US Patent 11,747,502, 2023 | | 2023 |
Karl TuylsResearch Scientist Meta AI, ex-Google DeepMind, Prof at University of Liverpoolmeta.com의 이메일 확인됨