Motion Perception in Reinforcement Learning with Dynamic Objects A Amiranashvili, A Dosovitskiy, V Koltun, T Brox Conference on Robot Learning (CoRL), 2018 | 35 | 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 | 30 | 2020 |
CrossNorm: On Normalization for Off-Policy Reinforcement Learning A Bhatt, M Argus, A Amiranashvili, T Brox | 26 | 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 | 21 | 2018 |
Scaling imitation learning in minecraft A Amiranashvili, N Dorka, W Burgard, V Koltun, T Brox arXiv preprint arXiv:2007.02701, 2020 | 16 | 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 |
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 |
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 | 4 | 2022 |
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, ... arXiv preprint arXiv:1902.05605, 2019 | 1 | 2019 |