Smaller World Models for Reinforcement Learning

no code implementations • 12 Oct 2020 • Jan Robine, Tobias Uelwer, Stefan Harmeling

Sample efficiency remains a fundamental issue of reinforcement learning.

Atari Games reinforcement-learning +1

Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm

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.

Transformer-based World Models Are Happy With 100k Interactions

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

A Survey on Self-Supervised Representation Learning

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.

object-detection Object Detection +1

Cyclophobic Reinforcement Learning

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.

Inductive Bias reinforcement-learning

Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data

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.

Computed Tomography (CT) Object

Backward Learning for Goal-Conditioned Policies

1 code implementation • 8 Dec 2023 • Marc Höftmann, Jan Robine, Stefan Harmeling

Can we learn policies in reinforcement learning without rewards?

Imitation Learning reinforcement-learning