Seuraa
Martin Riedmiller
Martin Riedmiller
DeepMind
Vahvistettu sähköpostiosoite verkkotunnuksessa google.com
Nimike
Viittaukset
Viittaukset
Vuosi
Human-level control through deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ...
nature 518 (7540), 529-533, 2015
290282015
Playing atari with deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ...
arXiv preprint arXiv:1312.5602, 2013
141352013
A direct adaptive method for faster backpropagation learning: The RPROP algorithm
M Riedmiller, H Braun
IEEE international conference on neural networks, 586-591, 1993
65411993
Striving for simplicity: The all convolutional net
JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller
arXiv preprint arXiv:1412.6806, 2014
55522014
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
International conference on machine learning, 387-395, 2014
47612014
Discriminative unsupervised feature learning with convolutional neural networks
A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox
Advances in neural information processing systems 27, 2014
18482014
Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method
M Riedmiller
Machine Learning: ECML 2005: 16th European Conference on Machine Learning …, 2005
13522005
Emergence of locomotion behaviours in rich environments
N Heess, D Tb, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ...
arXiv preprint arXiv:1707.02286, 2017
10572017
Playing atari with deep reinforcement learning. arXiv 2013
V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ...
arXiv preprint arXiv:1312.5602, 2013
8892013
Embed to control: A locally linear latent dynamics model for control from raw images
M Watter, J Springenberg, J Boedecker, M Riedmiller
Advances in neural information processing systems 28, 2015
8752015
Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms
M Riedmiller
Computer Standards & Interfaces 16 (3), 265-278, 1994
8381994
Multimodal deep learning for robust RGB-D object recognition
A Eitel, JT Springenberg, L Spinello, M Riedmiller, W Burgard
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015
7642015
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ...
arXiv preprint arXiv:1707.08817, 2017
7322017
Batch reinforcement learning
S Lange, T Gabel, M Riedmiller
Reinforcement learning: State-of-the-art, 45-73, 2012
6892012
Graph networks as learnable physics engines for inference and control
A Sanchez-Gonzalez, N Heess, JT Springenberg, J Merel, M Riedmiller, ...
International conference on machine learning, 4470-4479, 2018
6632018
An algorithm for distributed reinforcement learning in cooperative multi-agent systems
M Lauer, MA Riedmiller
Proceedings of the seventeenth international conference on machine learning …, 2000
6592000
Magnetic control of tokamak plasmas through deep reinforcement learning
J Degrave, F Felici, J Buchli, M Neunert, B Tracey, F Carpanese, T Ewalds, ...
Nature 602 (7897), 414-419, 2022
5832022
Rprop: a fast adaptive learning algorithm
M Riedmiller, H Braun
Proc. of the Int. Symposium on Computer and Information Science VII, 1992
5441992
Deepmind control suite
Y Tassa, Y Doron, A Muldal, T Erez, Y Li, DL Casas, D Budden, ...
arXiv preprint arXiv:1801.00690, 2018
5242018
Deep auto-encoder neural networks in reinforcement learning
S Lange, M Riedmiller
The 2010 international joint conference on neural networks (IJCNN), 1-8, 2010
4982010
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Artikkelit 1–20