3 papers
cs.LG2026
Relative Value Learning
Marc Höftmann, Jan Robine, Stefan Harmeling
In reinforcement learning, critics typically estimate absolute state values , estimating how good a particular situation is in isolation. However, it turns out that only diff…
cs.LG2025
Simple, Good, Fast: Self-Supervised World Models Free of Baggage
Jan Robine, Marc Höftmann, Stefan Harmeling
What are the essential components of world models? How far do we get with world models that are not employing RNNs, transformers, discrete representations, and image reconstruction…
cs.LG2024
Backward Learning for Goal-Conditioned Policies
Marc Höftmann, Jan Robine, Stefan Harmeling
Can we learn policies in reinforcement learning without rewards? Can we learn a policy just by trying to reach a goal state? We answer these questions positively by proposing a mul…