Transformer-based world models are happy with 100k interactions J Robine, M Höftmann, T Uelwer, S Harmeling arXiv preprint arXiv:2303.07109, 2023 | 37 | 2023 |
Smaller world models for reinforcement learning J Robine, T Uelwer, S Harmeling Neural Processing Letters 55 (8), 11397-11427, 2023 | 9* | 2023 |
Time-myopic go-explore: Learning a state representation for the go-explore paradigm M Höftmann, J Robine, S Harmeling arXiv preprint arXiv:2301.05635, 2023 | 3 | 2023 |
Limited-angle tomography reconstruction via deep end-to-end learning on synthetic data T Germer, J Robine, S Konietzny, S Harmeling, T Uelwer arXiv preprint arXiv:2309.06948, 2023 | 2 | 2023 |
A Survey on Self-Supervised Representation Learning T Uelwer, J Robine, SS Wagner, M Höftmann, E Upschulte, S Konietzny, ... arXiv preprint arXiv:2308.11455, 2023 | 2 | 2023 |
Backward Learning for Goal-Conditioned Policies M Höftmann, J Robine, S Harmeling arXiv preprint arXiv:2312.05044, 2023 | | 2023 |
Cyclophobic Reinforcement Learning SS Wagner, P Arndt, J Robine, S Harmeling arXiv preprint arXiv:2308.15911, 2023 | | 2023 |
Cyclophobic Reinforcement Learning S Sylvius Wagner, P Arndt, J Robine, S Harmeling arXiv e-prints, arXiv: 2308.15911, 2023 | | 2023 |
A Simple Framework for Self-Supervised Learning of Sample-Efficient World Models J Robine, M Höftmann, S Harmeling Dyna 31 (16), 17-18, 0 | | |