5 papers · 1 filter
Object-centric Denoising Diffusion Models for Physical Reasoning
Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen +2
Reasoning about the trajectories of multiple, interacting objects is integral to physical reasoning tasks in machine learning. This involves conditions imposed on the objects at di…
Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task
Raphael C. Engelhardt, Marcel J. Meinen, Moritz Lange +2
In previous research, we developed methods to train decision trees (DT) as agents for reinforcement learning tasks, based on deep reinforcement learning (DRL) networks. The samples…
ChatGPT Code Detection: Techniques for Uncovering the Source of Code
Marc Oedingen, Raphael C. Engelhardt, Robin Denz +2
In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by arti…
Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks
Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen +1
Visual navigation requires a whole range of capabilities. A crucial one of these is the ability of an agent to determine its own location and heading in an environment. Prior works…
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison
Moritz Lange, Noah Krystiniak, Raphael C. Engelhardt +2
Real-world reinforcement learning (RL) environments, whether in robotics or industrial settings, often involve non-visual observations and require not only efficient but also relia…