8 citations · 13 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data
Thomas Germer, Jan Robine, Sebastian Konietzny +2
Computed tomography (CT) has become an essential part of modern science and medicine. A CT scanner consists of an X-ray source that is spun around an object of interest. On the opp…
Cyclophobic Reinforcement Learning
Stefan Sylvius Wagner, Peter Arndt, Jan Robine +1
In environments with sparse rewards, finding a good inductive bias for exploration is crucial to the agent's success. However, there are two competing goals: novelty search and sys…
A Survey on Self-Supervised Representation Learning
Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner +5
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning. Recently, a lot of methods were introduced that allow learning of image r…
Transformer-based World Models Are Happy With 100k Interactions
Jan Robine, Marc Höftmann, Tobias Uelwer +1
Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient worl…
Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm
Marc Höftmann, Jan Robine, Stefan Harmeling
Very large state spaces with a sparse reward signal are difficult to explore. The lack of a sophisticated guidance results in a poor performance for numerous reinforcement learning…
Smaller World Models for Reinforcement Learning
Jan Robine, Tobias Uelwer, Stefan Harmeling
Sample efficiency remains a fundamental issue of reinforcement learning. Model-based algorithms try to make better use of data by simulating the environment with a model. We propos…