关注
Guy Lever
Guy Lever
未知所在单位机构
在 google.com 的电子邮件经过验证
标题
引用次数
引用次数
年份
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
International conference on machine learning, 387-395, 2014
48302014
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
15622017
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
9022019
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
1762017
Conditional mean embeddings as regressors-supplementary
S Grünewälder, G Lever, L Baldassarre, S Patterson, A Gretton, M Pontil
arXiv preprint arXiv:1205.4656, 2012
1662012
Emergent coordination through competition
S Liu, G Lever, J Merel, S Tunyasuvunakool, N Heess, T Graepel
arXiv preprint arXiv:1902.07151, 2019
1602019
Modelling transition dynamics in MDPs with RKHS embeddings
S Grunewalder, G Lever, L Baldassarre, M Pontil, A Gretton
arXiv preprint arXiv:1206.4655, 2012
1352012
Tighter PAC-Bayes bounds through distribution-dependent priors
G Lever, F Laviolette, J Shawe-Taylor
Theoretical Computer Science 473, 4-28, 2013
1252013
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
1012022
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
992019
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
792019
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
642009
Online prediction on large diameter graphs
M Herbster, G Lever, M Pontil
Advances in Neural Information Processing Systems 21, 2008
602008
Distribution-dependent PAC-Bayes priors
G Lever, F Laviolette, J Shawe-Taylor
International Conference on Algorithmic Learning Theory, 119-133, 2010
582010
Modelling policies in mdps in reproducing kernel hilbert space
G Lever, R Stafford
Artificial intelligence and statistics, 590-598, 2015
422015
Learning agile soccer skills for a bipedal robot with deep reinforcement learning
T Haarnoja, B Moran, G Lever, SH Huang, D Tirumala, M Wulfmeier, ...
arXiv preprint arXiv:2304.13653, 2023
362023
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
342016
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
302019
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
292016
The body is not a given: Joint agent policy learning and morphology evolution
D Banarse, Y Bachrach, S Liu, C Fernando, N Heess, P Kohli, G Lever, ...
142018
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