Shayegan Omidshafiei

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Jonathan P. HowRichard C. Maclaurin Professor of Aerospace Engineering, Massachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Karl TuylsResearch Scientist, Google DeepMind and Professor of computer science, University of LiverpoolDirección de correo verificada de google.com
Christopher AmatoAssociate Professor at Northeastern UniversityDirección de correo verificada de ccs.neu.edu
John VianDirección de correo verificada de ieee.org
julien perolatDeepMindDirección de correo verificada de google.com
Daniel HennesResearch Scientist, Google DeepMindDirección de correo verificada de google.com
Rémi MunosDeepMindDirección de correo verificada de inria.fr
Mark RowlandResearch Scientist, Google DeepMindDirección de correo verificada de google.com
Ali-akbar Agha-mohammadiNASA-JPL, CaltechDirección de correo verificada de jpl.nasa.gov
Marc LanctotResearch Scientist, DeepMindDirección de correo verificada de google.com
Miao LiuIBMDirección de correo verificada de ibm.com
Wojciech Marian Czarnecki.Dirección de correo verificada de google.com
Paul MullerResearch Scientist, DeepmindDirección de correo verificada de google.com
Dong Ki KimResearch Scientist, LG AI Research (previously: MIT)Dirección de correo verificada de lgresearch.ai
Shih-Yuan LiuDirector of Engineering - Planning, MotionalDirección de correo verificada de motional.com
Audrūnas GruslysDirección de correo verificada de gruslys.com
Georgios PiliourasSingapore University of Technology and DesignDirección de correo verificada de sutd.edu.sg
Nicolas HeessDeepMindDirección de correo verificada de google.com
Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCLDirección de correo verificada de ucl.ac.uk
Luke MarrisResearch Engineer at DeepMind, PhD Candidate and University College LondonDirección de correo verificada de ucl.ac.uk

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Shayegan Omidshafiei

Google Research (previously: DeepMind, MIT)

Dirección de correo verificada de google.com

Artificial Intelligence Machine Learning Reinforcement Learning Multi-agent Learning Game Theory


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	572	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	220	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	129	2019
α-Rank: Multi-Agent Evaluation by Evolution S Omidshafiei, C Papadimitriou, G Piliouras, K Tuyls, M Rowland, ... Scientific reports 9 (1), 9937, 2019	111	2019
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	86	2020
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	83	2022
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	82	2017
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	76	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	72	2021
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	71	2015
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	66*	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	63	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	52	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	40	2017
Multiagent evaluation under incomplete information M Rowland, S Omidshafiei, K Tuyls, J Perolat, M Valko, G Piliouras, ... arXiv preprint arXiv:1909.09849, 2019	35	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	34	2015
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	33	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	32	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	31	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	30	2019

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