| Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... arXiv preprint arXiv:1611.05397, 2016 | 694 | 2016 |
| Learning to reinforcement learn JX Wang, Z Kurth-Nelson, D Tirumala, H Soyer, JZ Leibo, R Munos, ... arXiv preprint arXiv:1611.05763, 2016 | 428 | 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 | 403 | 2018 |
| Multi-agent reinforcement learning in sequential social dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel arXiv preprint arXiv:1702.03037, 2017 | 325 | 2017 |
| 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 | 310 | 2019 |
| Deepmind lab C Beattie, JZ Leibo, D Teplyashin, T Ward, M Wainwright, H Küttler, ... arXiv preprint arXiv:1612.03801, 2016 | 279 | 2016 |
| 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 | 239* | 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 | 186 | 2018 |
| 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 | 185 | 2014 |
| Model-free episodic control C Blundell, B Uria, A Pritzel, Y Li, A Ruderman, JZ Leibo, J Rae, ... arXiv preprint arXiv:1606.04460, 2016 | 155* | 2016 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... | 127 | 2017 |
| 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, 4331-4339, 2016 | 120 | 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 | 96* | 2019 |
| 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 | 92 | 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 | 92 | 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, 3643-3652, 2017 | 89 | 2017 |
| How important is weight symmetry in backpropagation? Q Liao, J Leibo, T Poggio Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 79 | 2016 |
| Unsupervised learning of invariant representations in hierarchical architectures F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio arXiv preprint arXiv:1311.4158, 2013 | 74 | 2013 |
| Emergent communication through negotiation K Cao, A Lazaridou, M Lanctot, JZ Leibo, K Tuyls, S Clark arXiv preprint arXiv:1804.03980, 2018 | 68 | 2018 |
| 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, 3326-3336, 2018 | 61 | 2018 |
TOMASO POGGIOMcDermott Professor in Brain Sciences, MITVerified email at ai.mit.edu
