Martin Riedmiller
Martin Riedmiller
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Cited by
Cited by
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
Machine Learning: ECML 2005: 16th European Conference on Machine Learning …, 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
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
Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms
M Riedmiller
Computer Standards & Interfaces 16 (3), 265-278, 1994
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
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
Batch reinforcement learning
S Lange, T Gabel, M Riedmiller
Reinforcement learning: State-of-the-art, 45-73, 2012
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
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
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
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
Deepmind control suite
Y Tassa, Y Doron, A Muldal, T Erez, Y Li, DL Casas, D Budden, ...
arXiv preprint arXiv:1801.00690, 2018
Deep auto-encoder neural networks in reinforcement learning
S Lange, M Riedmiller
The 2010 international joint conference on neural networks (IJCNN), 1-8, 2010
Maximum a posteriori policy optimisation
A Abdolmaleki, JT Springenberg, Y Tassa, R Munos, N Heess, ...
arXiv preprint arXiv:1806.06920, 2018
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