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| Playing atari with deep reinforcement learning V Mnih arXiv preprint arXiv:1312.5602, 2013 | 15788 | 2013 |
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| Striving for simplicity: The all convolutional net JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller arXiv preprint arXiv:1412.6806, 2014 | 5954 | 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 | 5292 | 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 | 1972 | 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 | 1422 | 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 | 1127 | 2017 |
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| 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 | 937 | 2015 |
| Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms M Riedmiller Computer Standards & Interfaces 16 (3), 265-278, 1994 | 848 | 1994 |
| 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 | 814 | 2017 |
| 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 | 791 | 2015 |
| 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 | 772 | 2022 |
| Batch reinforcement learning S Lange, T Gabel, M Riedmiller Reinforcement learning: State-of-the-art, 45-73, 2012 | 755 | 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 | 717 | 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 | 686 | 2000 |
| Deepmind control suite Y Tassa, Y Doron, A Muldal, T Erez, Y Li, DL Casas, D Budden, ... arXiv preprint arXiv:1801.00690, 2018 | 621 | 2018 |
| Rprop: a fast adaptive learning algorithm M Riedmiller, H Braun Proc. of the Int. Symposium on Computer and Information Science VII, 1992 | 559 | 1992 |
| Deep auto-encoder neural networks in reinforcement learning S Lange, M Riedmiller The 2010 international joint conference on neural networks (IJCNN), 1-8, 2010 | 520 | 2010 |
