Martin Riedmiller
Martin Riedmiller
Professor of Computer Science, Albert-Ludwigs-Universitaet Freiburg
Verified email at informatik.uni-freiburg.de
TitleCited byYear
Human-level control through deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ...
Nature 518 (7540), 529, 2015
75932015
A direct adaptive method for faster backpropagation learning: The RPROP algorithm
M Riedmiller, H Braun
Proceedings of the IEEE international conference on neural networks 1993 …, 1993
51821993
Playing atari with deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ...
arXiv preprint arXiv:1312.5602, 2013
31742013
Striving for simplicity: The all convolutional net
JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller
arXiv preprint arXiv:1412.6806, 2014
16602014
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
9712014
Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method
M Riedmiller
European Conference on Machine Learning, 317-328, 2005
6852005
Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms
M Riedmiller
Computer Standards & Interfaces 16 (3), 265-278, 1994
5871994
RPROP-A fast adaptive learning algorithm
M Riedmiller, H Braun
Proc. of ISCIS VII), Universitat, 1992
4041992
An algorithm for distributed reinforcement learning in cooperative multi-agent systems
M Lauer, M Riedmiller
In Proceedings of the Seventeenth International Conference on Machine Learning, 2000
3812000
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
3702015
Rprop-description and implementation details
M Riedmiller, I Rprop
3171994
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, 2746-2754, 2015
3002015
Discriminative unsupervised feature learning with convolutional neural networks
A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox
Advances in neural information processing systems, 766-774, 2014
2992014
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
2702017
Reinforcement learning for robot soccer
M Riedmiller, T Gabel, R Hafner, S Lange
Autonomous Robots 27 (1), 55-73, 2009
2352009
A learned feature descriptor for object recognition in rgb-d data
M Blum, JT Springenberg, J Wülfing, M Riedmiller
2012 IEEE International Conference on Robotics and Automation, 1298-1303, 2012
2102012
Deep auto-encoder neural networks in reinforcement learning
S Lange, M Riedmiller
The 2010 International Joint Conference on Neural Networks (IJCNN), 1-8, 2010
2072010
Distributed value functions
J Schneider, WK Wong, A Moore, M Riedmiller
ICML, 371-378, 1999
1971999
Discriminative unsupervised feature learning with exemplar convolutional neural networks
A Dosovitskiy, P Fischer, JT Springenberg, M Riedmiller, T Brox
IEEE transactions on pattern analysis and machine intelligence 38 (9), 1734-1747, 2015
1432015
Autonomous reinforcement learning on raw visual input data in a real world application
S Lange, M Riedmiller, A Voigtländer
The 2012 International Joint Conference on Neural Networks (IJCNN), 1-8, 2012
1392012
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