| Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability S Omidshafiei, J Pazis, C Amato, JP How, J Vian Proceedings of the 34th International Conference on Machine Learning (ICML …, 2017 | 686 | 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 | 335 | 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 | 273 | 2022 |
| Learning to Teach in Cooperative Multiagent Reinforcement Learning S Omidshafiei, DK Kim, M Liu, G Tesauro, M Riemer, C Amato, ... AAAI 2019, Best Student Paper Honorable Mention, 2019 | 169 | 2019 |
| α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019 | 151 | 2019 |
| 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 | 137 | 2022 |
| Real World Games Look Like Spinning Tops WM Czarnecki, G Gidel, B Tracey, K Tuyls, S Omidshafiei, D Balduzzi, ... Conference on Neural Information Processing Systems (NeurIPS), 2020 | 123 | 2020 |
| A generalized training approach for multiagent learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... International Conference on Learning Representations (ICLR), 2020 | 123 | 2020 |
| 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 | 114 | 2021 |
| 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 | 102 | 2021 |
| Neural replicator dynamics: Multiagent learning via hedging policy gradients D Hennes, D Morrill, S Omidshafiei, R Munos, J Perolat, M Lanctot, ... Proceedings of the 19th international conference on autonomous agents and …, 2020 | 97* | 2020 |
| Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions S Omidshafiei, AA Agha–Mohammadi, C Amato, SY Liu, JP How, J Vian The International Journal of Robotics Research (IJRR), 0278364917692864, 2017 | 93 | 2017 |
| Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions S Omidshafiei, A Agha-mohammadi, C Amato, JP How IEEE International Conference on Robotics and Automation (ICRA), 5962-5969, 2015 | 81 | 2015 |
| Toward general-purpose robots via foundation models: A survey and meta-analysis Y Hu, Q Xie, V Jain, J Francis, J Patrikar, N Keetha, S Kim, Y Xie, T Zhang, ... arXiv preprint arXiv:2312.08782, 2023 | 77 | 2023 |
| Navigating the landscape of multiplayer games S Omidshafiei, K Tuyls, WM Czarnecki, FC Santos, M Rowland, J Connor, ... Nature communications 11 (1), 5603, 2020 | 52 | 2020 |
| Simultaneous mapping and planning by a robot A Aghamohammadi, SD Spindola, BF Behabadi, C Lott, S Omidshafiei, ... US Patent 10,093,021, 2018 | 48 | 2018 |
| Multiagent evaluation under incomplete information M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ... arXiv preprint arXiv:1909.09849, 2019 | 45 | 2019 |
| Learning hierarchical teaching policies for cooperative agents DK Kim, M Liu, S Omidshafiei, S Lopez-Cot, M Riemer, G Habibi, ... arXiv preprint arXiv:1903.03216, 2019 | 40 | 2019 |
| Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions M Liu, K Sivakumar, S Omidshafiei, C Amato, JP How IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017, 2017 | 40 | 2017 |
| Policy distillation and value matching in multiagent reinforcement learning S Wadhwania, DK Kim, S Omidshafiei, JP How 2019 IEEE/RSJ international conference on intelligent robots and systems …, 2019 | 37 | 2019 |
Jonathan P. HowRichard C. Maclaurin Professor of Aerospace Engineering, Massachusetts Institute of TechnologyEmail confirmado em mit.edu