Discrete Latent Space World Models for Reinforcement Learning. - Jan Robine; Tobias Uelwer; Stefan Harmeling (CoRR, 2020)

Transformer-based World Models Are Happy With 100k Interactions. - Jan Robine; Marc Höftmann; Tobias Uelwer; Stefan Harmeling (ICLR, 2023)

Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm. - Marc Höftmann; Jan Robine; Stefan Harmeling (CoRR, 2023)

A Survey on Self-Supervised Representation Learning. - Tobias Uelwer; Jan Robine; Stefan Sylvius Wagner; Marc Höftmann; Eric Upschulte; Sebastian Konietzny; Maike Behrendt; Stefan Harmeling (CoRR, 2023)

Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data. - Thomas Germer; Jan Robine; Sebastian Konietzny; Stefan Harmeling; Tobias Uelwer (CoRR, 2023)

Backward Learning for Goal-Conditioned Policies. - Marc Höftmann; Jan Robine; Stefan Harmeling (CoRR, 2023)

Cyclophobic Reinforcement Learning. - Stefan Sylvius Wagner; Peter Arndt 001; Jan Robine; Stefan Harmeling (Trans. Mach. Learn. Res., 2023)

Smaller World Models for Reinforcement Learning. - Jan Robine; Tobias Uelwer; Stefan Harmeling (Neural Process. Lett., 2023)

Simple, Good, Fast: Self-Supervised World Models Free of Baggage. - Jan Robine; Marc Höftmann; Stefan Harmeling (ICLR, 2025)

A survey on self-supervised methods for visual representation learning. - Tobias Uelwer; Jan Robine; Stefan Sylvius Wagner; Marc Höftmann; Eric Upschulte; Sebastian Konietzny; Maike Behrendt; Stefan Harmeling (Mach. Learn., 2025)