| Deterministic policy gradient algorithms D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller International conference on machine learning, 387-395, 2014 | 5480 | 2014 |
| 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 | 1923 | 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 | 1010 | 2019 |
| Nesterov's accelerated gradient and momentum as approximations to regularised update descent A Botev, G Lever, D Barber 2017 International joint conference on neural networks (IJCNN), 1899-1903, 2017 | 198 | 2017 |
| Conditional mean embeddings as regressors-supplementary S Grünewälder, G Lever, L Baldassarre, S Patterson, A Gretton, M Pontil arXiv preprint arXiv:1205.4656, 2012 | 182 | 2012 |
| Emergent coordination through competition S Liu, G Lever, J Merel, S Tunyasuvunakool, N Heess, T Graepel arXiv preprint arXiv:1902.07151, 2019 | 171 | 2019 |
| Modelling transition dynamics in MDPs with RKHS embeddings S Grunewalder, G Lever, L Baldassarre, M Pontil, A Gretton arXiv preprint arXiv:1206.4655, 2012 | 144 | 2012 |
| Tighter PAC-Bayes bounds through distribution-dependent priors G Lever, F Laviolette, J Shawe-Taylor Theoretical Computer Science 473, 4-28, 2013 | 134 | 2013 |
| 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 | 118 | 2022 |
| A generalized training approach for multiagent learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... arXiv preprint arXiv:1909.12823, 2019 | 116 | 2019 |
| Learning agile soccer skills for a bipedal robot with deep reinforcement learning T Haarnoja, B Moran, G Lever, SH Huang, D Tirumala, J Humplik, ... Science Robotics 9 (89), eadi8022, 2024 | 100 | 2024 |
| Biases for emergent communication in multi-agent reinforcement learning T Eccles, Y Bachrach, G Lever, A Lazaridou, T Graepel Advances in neural information processing systems 32, 2019 | 87 | 2019 |
| Predicting the labelling of a graph via minimum p-seminorm interpolation M Herbster, G Lever NIPS Workshop 2010: Networks Across Disciplines: Theory and Applications, 2009 | 68 | 2009 |
| Online prediction on large diameter graphs M Herbster, G Lever, M Pontil Advances in Neural Information Processing Systems 21, 2008 | 61 | 2008 |
| Distribution-dependent PAC-Bayes priors G Lever, F Laviolette, J Shawe-Taylor International Conference on Algorithmic Learning Theory, 119-133, 2010 | 60 | 2010 |
| Modelling policies in mdps in reproducing kernel hilbert space G Lever, R Stafford Artificial intelligence and statistics, 590-598, 2015 | 44 | 2015 |
| Approximate newton methods for policy search in markov decision processes T Furmston, G Lever, D Barber Journal of Machine Learning Research 17 (226), 1-51, 2016 | 40 | 2016 |
| Reinforcement learning agents acquire flocking and symbiotic behaviour in simulated ecosystems P Sunehag, G Lever, S Liu, J Merel, N Heess, JZ Leibo, E Hughes, ... Artificial life conference proceedings, 103-110, 2019 | 33 | 2019 |
| Compressed conditional mean embeddings for model-based reinforcement learning G Lever, J Shawe-Taylor, R Stafford, C Szepesvári Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 31 | 2016 |
| The body is not a given: Joint agent policy learning and morphology evolution D Banarse, Y Bachrach, S Liu, G Lever, N Heess, C Fernando, P Kohli, ... AAMAS'19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS …, 2019 | 14 | 2019 |
