| 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 | 1940 | 2017 |
| 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 | 1493* | 2018 |
| Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... arXiv preprint arXiv:1611.05397, 2016 | 1453 | 2016 |
| Learning to reinforcement learn JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ... arXiv preprint arXiv:1611.05763, 2016 | 1129 | 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 | 1019 | 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 | 936 | 2017 |
| 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 | 694 | 2018 |
| Deepmind lab C Beattie, JZ Leibo, D Teplyashin, T Ward, M Wainwright, H Küttler, ... arXiv preprint arXiv:1612.03801, 2016 | 630 | 2016 |
| 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 | 586 | 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 | 307 | 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 | 288 | 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 | 284 | 2016 |
| 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 | 274 | 2018 |
| 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 | 244 | 2020 |
| 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 | 233 | 2017 |
| 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 | 207 | 2018 |
| Emergent communication through negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018 | 196 | 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 | 186 | 2016 |
| Kickstarting deep reinforcement learning S Schmitt, JJ Hudson, A Zidek, S Osindero, C Doersch, WM Czarnecki, ... arXiv preprint arXiv:1803.03835, 2018 | 154 | 2018 |
| Unsupervised learning of invariant representations F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio Theoretical Computer Science 633, 112-121, 2016 | 151 | 2016 |
Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCL在 ucl.ac.uk 的电子邮件经过验证
