| 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 | 23468 | 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 | 11404 | 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 | 6453 | 1993 |
| Striving for simplicity: The all convolutional net JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller arXiv preprint arXiv:1412.6806, 2014 | 4720 | 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 | 3785 | 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 | 1599 | 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 | 1237 | 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 | 895 | 2017 |
| Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms M Riedmiller Computer Standards & Interfaces 16 (3), 265-278, 1994 | 793 | 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 | 749 | 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 | 705 | 2015 |
| An algorithm for distributed reinforcement learning in cooperative multiagent systems M Lauer Proc. 17th International Conf. on Machine Learning, 2000 | 605 | 2000 |
| 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 | 573 | 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 | 527 | 2018 |
| Batch reinforcement learning S Lange, T Gabel, M Riedmiller Reinforcement learning: State-of-the-art, 45-73, 2012 | 527 | 2012 |
| 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 | 523 | 2013 |
| Deep auto-encoder neural networks in reinforcement learning S Lange, M Riedmiller The 2010 international joint conference on neural networks (IJCNN), 1-8, 2010 | 431 | 2010 |
| Rprop-description and implementation details M Riedmiller, H Braun Technical Report. Univer, 1994 | 400 | 1994 |
| Reinforcement learning for robot soccer M Riedmiller, T Gabel, R Hafner, S Lange Autonomous Robots 27, 55-73, 2009 | 371 | 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 | 368 | 2018 |
