Martin Riedmiller
Martin Riedmiller
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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
Playing atari with deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ...
arXiv preprint arXiv:1312.5602, 2013
A direct adaptive method for faster backpropagation learning: The RPROP algorithm
M Riedmiller, H Braun
IEEE international conference on neural networks, 586-591, 1993
Striving for simplicity: The all convolutional net
JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller
arXiv preprint arXiv:1412.6806, 2014
Deterministic policy gradient algorithms
D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller
International conference on machine learning, 387-395, 2014
Discriminative unsupervised feature learning with convolutional neural networks
A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox
Advances in neural information processing systems 27, 2014
Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method
M Riedmiller
European conference on machine learning, 317-328, 2005
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
Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms
M Riedmiller
Computer Standards & Interfaces 16 (3), 265-278, 1994
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
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
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
Rprop-a fast adaptive learning algorithm
M Riedmiller, H Braun
Proc. of ISCIS VII), Universitat, 1992
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
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
Batch reinforcement learning
S Lange, T Gabel, M Riedmiller
Reinforcement learning, 45-73, 2012
Deep auto-encoder neural networks in reinforcement learning
S Lange, M Riedmiller
The 2010 international joint conference on neural networks (IJCNN), 1-8, 2010
Rprop-description and implementation details
M Riedmiller, I Rprop
Reinforcement learning for robot soccer
M Riedmiller, T Gabel, R Hafner, S Lange
Autonomous Robots 27 (1), 55-73, 2009
Learning by playing solving sparse reward tasks from scratch
M Riedmiller, R Hafner, T Lampe, M Neunert, J Degrave, T Wiele, V Mnih, ...
International conference on machine learning, 4344-4353, 2018
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