2 code implementations • 22 Aug 2023 • Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner, Marc Höftmann, Eric Upschulte, Sebastian Konietzny, Maike Behrendt, Stefan Harmeling
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning.
1 code implementation • 13 Mar 2023 • Jan Robine, Marc Höftmann, Tobias Uelwer, Stefan Harmeling
Deep neural networks have been successful in many reinforcement learning settings.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • 13 Jan 2023 • Marc Höftmann, Jan Robine, Stefan Harmeling
Very large state spaces with a sparse reward signal are difficult to explore.
no code implementations • 12 Oct 2020 • Jan Robine, Tobias Uelwer, Stefan Harmeling
Sample efficiency remains a fundamental issue of reinforcement learning.
Ranked #27 on Atari Games on Atari 2600 Pong
no code implementations • 30 Aug 2023 • Stefan Sylvius Wagner, Peter Arndt, Jan Robine, Stefan Harmeling
In environments with sparse rewards, finding a good inductive bias for exploration is crucial to the agent's success.
1 code implementation • 13 Sep 2023 • Thomas Germer, Jan Robine, Sebastian Konietzny, Stefan Harmeling, Tobias Uelwer
A CT scanner consists of an X-ray source that is spun around an object of interest.
1 code implementation • 8 Dec 2023 • Marc Höftmann, Jan Robine, Stefan Harmeling
Can we learn policies in reinforcement learning without rewards?