| Superhuman AI for multiplayer poker N Brown, T Sandholm Science 365 (6456), 885-890, 2019 | 1011 | 2019 |
| Superhuman AI for heads-up no-limit poker: Libratus beats top professionals N Brown, T Sandholm Science 359 (6374), 418-424, 2018 | 960 | 2018 |
| Deep counterfactual regret minimization N Brown, A Lerer, S Gross, T Sandholm International Conference on Machine Learning, 2019 | 292 | 2019 |
| Human-level play in the game of Diplomacy by combining language models with strategic reasoning Meta Fundamental AI Research Diplomacy Team (FAIR)†, A Bakhtin, ... Science 378 (6624), 1067-1074, 2022 | 243 | 2022 |
| Safe and nested subgame solving for imperfect-information games N Brown, T Sandholm Neural Information Processing Systems, 2017 | 236* | 2017 |
| Solving imperfect-information games via discounted regret minimization N Brown, T Sandholm Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1829-1836, 2019 | 189 | 2019 |
| Combining deep reinforcement learning and search for imperfect-information games N Brown, A Bakhtin, A Lerer, Q Gong Advances in Neural Information Processing Systems 33, 17057-17069, 2020 | 171 | 2020 |
| Libratus: The Superhuman AI for No-Limit Poker. N Brown, T Sandholm IJCAI, 5226-5228, 2017 | 155 | 2017 |
| Depth-limited solving for imperfect-information games N Brown, T Sandholm, B Amos Advances in neural information processing systems 31, 2018 | 101 | 2018 |
| Improving Policies via Search in Cooperative Partially Observable Games A Lerer, H Hu, J Foerster, N Brown AAAI Conference on Artificial Intelligence, 2020 | 87 | 2020 |
| Hierarchical abstraction, distributed equilibrium computation, and post-processing, with application to a champion no-limit Texas Hold'em agent N Brown, S Ganzfried, T Sandholm Workshops at the twenty-ninth AAAI conference on artificial intelligence, 2015 | 87 | 2015 |
| Off-belief learning H Hu, A Lerer, B Cui, L Pineda, N Brown, J Foerster International Conference on Machine Learning, 4369-4379, 2021 | 70 | 2021 |
| A unified approach to reinforcement learning, quantal response equilibria, and two-player zero-sum games S Sokota, R D'Orazio, JZ Kolter, N Loizou, M Lanctot, I Mitliagkas, ... arXiv preprint arXiv:2206.05825, 2022 | 61 | 2022 |
| Modeling strong and human-like gameplay with KL-regularized search AP Jacob, DJ Wu, G Farina, A Lerer, H Hu, A Bakhtin, J Andreas, N Brown International Conference on Machine Learning, 9695-9728, 2022 | 59 | 2022 |
| Dream: Deep regret minimization with advantage baselines and model-free learning E Steinberger, A Lerer, N Brown arXiv preprint arXiv:2006.10410, 2020 | 59 | 2020 |
| Human-level performance in no-press diplomacy via equilibrium search J Gray, A Lerer, A Bakhtin, N Brown arXiv preprint arXiv:2010.02923, 2020 | 56 | 2020 |
| Dynamic thresholding and pruning for regret minimization N Brown, C Kroer, T Sandholm Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 54 | 2017 |
| No-press diplomacy from scratch A Bakhtin, D Wu, A Lerer, N Brown Advances in Neural Information Processing Systems 34, 18063-18074, 2021 | 46 | 2021 |
| Mastering the game of no-press Diplomacy via human-regularized reinforcement learning and planning A Bakhtin, DJ Wu, A Lerer, J Gray, AP Jacob, G Farina, AH Miller, ... arXiv preprint arXiv:2210.05492, 2022 | 45 | 2022 |
| Stable-predictive optimistic counterfactual regret minimization G Farina, C Kroer, N Brown, T Sandholm International conference on machine learning, 1853-1862, 2019 | 43 | 2019 |