Joel Z Leibo

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TOMASO POGGIOMcDermott Professor in Brain Sciences, MITPatvirtintas el. paštas ai.mit.edu
Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCLPatvirtintas el. paštas ucl.ac.uk
Edward HughesStaff Research Engineer, DeepMindPatvirtintas el. paštas google.com
Marc LanctotResearch Scientist, DeepMindPatvirtintas el. paštas google.com
Wojciech Marian Czarnecki.Patvirtintas el. paštas google.com
Karl TuylsResearch Scientist, Google DeepMind and Professor of computer science, University of LiverpoolPatvirtintas el. paštas google.com
Matthew BotvinickGoogle DeepMindPatvirtintas el. paštas google.com
Charlie BeattieSoftware Engineer, DeepMindPatvirtintas el. paštas google.com
Edgar A. Duéñez-GuzmánGoogle DeepMindPatvirtintas el. paštas oeb.harvard.edu
Peter SunehagGoogle - DeepMindPatvirtintas el. paštas google.com
Tom SchaulSenior Staff Scientist, DeepMindPatvirtintas el. paštas nyu.edu
Fabio AnselmiAssistant professor at University of Trieste, MIT affiliatePatvirtintas el. paštas units.it
Max JaderbergIsomorphic LabsPatvirtintas el. paštas robots.ox.ac.uk
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaPatvirtintas el. paštas unige.it
Jane X. WangStaff Research Scientist, DeepMindPatvirtintas el. paštas google.com
Dharshan KumaranGoogle DeepMindPatvirtintas el. paštas fil.ion.ucl.ac.uk
koray kavukcuogluDeepMindPatvirtintas el. paštas kavukcuoglu.org
David SilverDeepMind, UCLPatvirtintas el. paštas google.com
Kevin R. McKeeResearch Scientist, DeepMindPatvirtintas el. paštas deepmind.com
Vinicius ZambaldiGoogle DeepmindPatvirtintas el. paštas google.com

Stebėti

Joel Z Leibo

Research scientist

Patvirtintas el. paštas google.com - Pagrindinis puslapis

Multi-Agent Reinforcement Learning Machine Learning Deep Reinforcement Learning Computational Neuroscience Vision


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... arXiv preprint arXiv:1611.05397, 2016	1191	2016
Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	1099*	2018
Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017	1085	2017
Learning to reinforcement learn JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ... arXiv preprint arXiv:1611.05763, 2016	863	2016
Human-level performance in 3D multiplayer games with population-based reinforcement learning M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ... Science 364 (6443), 859-865, 2019	741	2019
Multi-agent reinforcement learning in sequential social dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel arXiv preprint arXiv:1702.03037, 2017	698	2017
Deepmind lab C Beattie, JZ Leibo, D Teplyashin, T Ward, M Wainwright, H Küttler, ... arXiv preprint arXiv:1612.03801, 2016	497	2016
Prefrontal cortex as a meta-reinforcement learning system JX Wang, Z Kurth-Nelson, D Kumaran, D Tirumala, H Soyer, JZ Leibo, ... Nature neuroscience 21 (6), 860-868, 2018	495	2018
Social influence as intrinsic motivation for multi-agent deep reinforcement learning N Jaques, A Lazaridou, E Hughes, C Gulcehre, P Ortega, DJ Strouse, ... International conference on machine learning, 3040-3049, 2019	377*	2019
Model-free episodic control C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ... arXiv preprint arXiv:1606.04460, 2016	257*	2016
The dynamics of invariant object recognition in the human visual system L Isik, EM Meyers, JZ Leibo, T Poggio Journal of neurophysiology 111 (1), 91-102, 2014	247	2014
Using fast weights to attend to the recent past J Ba, GE Hinton, V Mnih, JZ Leibo, C Ionescu Advances in neural information processing systems 29, 2016	215	2016
A multi-agent reinforcement learning model of common-pool resource appropriation J Perolat, JZ Leibo, V Zambaldi, C Beattie, K Tuyls, T Graepel Advances in neural information processing systems 30, 2017	182	2017
Inequity aversion improves cooperation in intertemporal social dilemmas E Hughes, JZ Leibo, M Phillips, K Tuyls, E Dueñez-Guzman, ... Advances in neural information processing systems 31, 2018	177	2018
Unsupervised predictive memory in a goal-directed agent G Wayne, CC Hung, D Amos, M Mirza, A Ahuja, A Grabska-Barwinska, ... arXiv preprint arXiv:1803.10760, 2018	170	2018
Emergent communication through negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018	156	2018
How important is weight symmetry in backpropagation? Q Liao, J Leibo, T Poggio Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016	136	2016
Unsupervised learning of invariant representations F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio Theoretical Computer Science 633, 112-121, 2016	129	2016
Open problems in cooperative AI A Dafoe, E Hughes, Y Bachrach, T Collins, KR McKee, JZ Leibo, K Larson, ... arXiv preprint arXiv:2012.08630, 2020	123	2020
Kickstarting deep reinforcement learning S Schmitt, JJ Hudson, A Zidek, S Osindero, C Doersch, WM Czarnecki, ... arXiv preprint arXiv:1803.03835, 2018	109	2018

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