162 citations · 177 across the 9 of their papers we have counts for
4 papers · 1 filter
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…