| Motion Perception in Reinforcement Learning with Dynamic Objects A Amiranashvili, A Dosovitskiy, V Koltun, T Brox Conference on Robot Learning (CoRL), 2018 | 37 | 2018 |
| Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control L Hermann, M Argus, A Eitel, A Amiranashvili, W Burgard, T Brox IEEE International Conference on Robotics and Automation (ICRA), 2020 | 31 | 2020 |
| Crossnorm: Normalization for off-policy td reinforcement learning A Bhatt, M Argus, A Amiranashvili, T Brox arXiv preprint arXiv:1902.05605 10, 2019 | 29 | 2019 |
| TD or not TD: Analyzing the role of temporal differencing in deep reinforcement learning A Amiranashvili, A Dosovitskiy, V Koltun, T Brox International Conference on Learning Representations (ICLR), 2018 | 22 | 2018 |
| Scaling imitation learning in minecraft A Amiranashvili, N Dorka, W Burgard, V Koltun, T Brox arXiv preprint arXiv:2007.02701, 2020 | 17 | 2020 |
| Stochastic switching between multistable oscillation patterns of the Min-system A Amiranashvili, ND Schnellbächer, US Schwarz New Journal of Physics 18 (9), 093049, 2016 | 12 | 2016 |
| CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity A Bhatt, D Palenicek, B Belousov, M Argus, A Amiranashvili, T Brox, ... International Conference on Learning Representations (ICLR), 2024 | 7* | 2024 |
| A benchmark and a baseline for robust multi-view depth estimation P Schröppel, J Bechtold, A Amiranashvili, T Brox 2022 International Conference on 3D Vision (3DV), 637-645, 2022 | 7 | 2022 |
| Pre-training of deep rl agents for improved learning under domain randomization A Amiranashvili, M Argus, L Hermann, W Burgard, T Brox arXiv preprint arXiv:2104.14386, 2021 | 5 | 2021 |
