Nicolas Heess
Nicolas Heess
DeepMind
Verifierad e-postadress på google.com
TitelCiteras avÅr
Continuous control with deep reinforcement learning
TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ...
arXiv preprint arXiv:1509.02971, 2015
29882015
Recurrent models of visual attention
V Mnih, N Heess, A Graves
Advances in neural information processing systems, 2204-2212, 2014
15912014
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
ICML, 2014
11582014
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
4452018
Emergence of locomotion behaviours in rich environments
N Heess, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ...
arXiv preprint arXiv:1707.02286, 2017
3122017
Sample efficient actor-critic with experience replay
Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ...
arXiv preprint arXiv:1611.01224, 2016
3002016
Feudal networks for hierarchical reinforcement learning
AS Vezhnevets, S Osindero, T Schaul, N Heess, M Jaderberg, D Silver, ...
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
2952017
Learning continuous control policies by stochastic value gradients
N Heess, G Wayne, D Silver, T Lillicrap, T Erez, Y Tassa
Advances in Neural Information Processing Systems, 2944-2952, 2015
2632015
Sim-to-real robot learning from pixels with progressive nets
AA Rusu, M Vecerik, T Rothörl, N Heess, R Pascanu, R Hadsell
arXiv preprint arXiv:1610.04286, 2016
2402016
Imagination-augmented agents for deep reinforcement learning
T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ...
arXiv preprint arXiv:1707.06203, 2017
230*2017
Imagination-augmented agents for deep reinforcement learning
S Racanière, T Weber, D Reichert, L Buesing, A Guez, DJ Rezende, ...
Advances in neural information processing systems, 5690-5701, 2017
2302017
Unsupervised learning of 3D structure from images
D Jimenez Rezende, SM Eslami, S Mohamed, P Battaglia, M Jaderberg, ...
arXiv preprint arXiv:1607.00662, 2016
207*2016
Unsupervised learning of 3d structure from images
DJ Rezende, SMA Eslami, S Mohamed, P Battaglia, M Jaderberg, ...
Advances in Neural Information Processing Systems, 4996-5004, 2016
2072016
Attend, infer, repeat: Fast scene understanding with generative models
SMA Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton
Advances in Neural Information Processing Systems, 3225-3233, 2016
2062016
Gradient estimation using stochastic computation graphs
J Schulman, N Heess, T Weber, P Abbeel
Advances in Neural Information Processing Systems, 3528-3536, 2015
1952015
The shape boltzmann machine: a strong model of object shape
SMA Eslami, N Heess, CKI Williams, J Winn
International Journal of Computer Vision 107 (2), 155-176, 2014
1932014
Distral: Robust multitask reinforcement learning
Y Teh, V Bapst, WM Czarnecki, J Quan, J Kirkpatrick, R Hadsell, N Heess, ...
Advances in Neural Information Processing Systems, 4496-4506, 2017
1632017
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M Večerík, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ...
arXiv preprint arXiv:1707.08817, 2017
1282017
Graph networks as learnable physics engines for inference and control
A Sanchez-Gonzalez, N Heess, JT Springenberg, J Merel, M Riedmiller, ...
arXiv preprint arXiv:1806.01242, 2018
1052018
Learning by playing-solving sparse reward tasks from scratch
M Riedmiller, R Hafner, T Lampe, M Neunert, J Degrave, T Van de Wiele, ...
arXiv preprint arXiv:1802.10567, 2018
1042018
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Artiklar 1–20