| 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 | 486 | 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 | 171 | 2019 |
| 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 | 105 | 2019 |
| α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019 | 95 | 2019 |
| 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 | 74 | 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 | 62 | 2015 |
| 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 | 60 | 2020 |
| 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 | 56 | 2020 |
| 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 | 54* | 2020 |
| 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 | 50 | 2021 |
| 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 | 43 | 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 | 36 | 2021 |
| 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 | 34 | 2017 |
| Measurable augmented reality for prototyping cyberphysical systems: A robotics platform to aid the hardware prototyping and performance testing of algorithms S Omidshafiei, AA Agha-Mohammadi, YF Chen, NK Ure, SY Liu, ... IEEE Control Systems 36 (6), 65-87, 2016 | 32 | 2016 |
| Multiagent evaluation under incomplete information M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ... arXiv preprint arXiv:1909.09849, 2019 | 31 | 2019 |
| MAR-CPS: Measurable augmented reality for prototyping cyber-physical systems S Omidshafiei, AA Agha-Mohammadi, YF Chen, NK Üre, JP How, JL Vian, ... AIAA Infotech@ Aerospace, 0643, 2015 | 31 | 2015 |
| Navigating the landscape of multiplayer games S Omidshafiei, K Tuyls, WM Czarnecki, FC Santos, M Rowland, J Connor, ... Nature communications 11 (1), 5603, 2020 | 28 | 2020 |
| Graph-based Cross Entropy Method for Solving Multi-Robot Decentralized POMDPs S Omidshafiei, A Agha-mohammadi, C Amato, SY Liu, JP How, J Vian IEEE International Conference on Robotics and Automation (ICRA), 5395-5402, 2016 | 28 | 2016 |
| 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 | 23 | 2019 |
| Camera control for learning nonlinear target dynamics via Bayesian nonparametric Dirichlet-process Gaussian-process (DP-GP) models H Wei, W Lu, P Zhu, S Ferrari, RH Klein, S Omidshafiei, JP How 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 95-102, 2014 | 22 | 2014 |
Jonathan P. HowRichard C. Maclaurin Professor of Aerospace Engineering, Massachusetts Institute of TechnologyVerified email at mit.edu
