4 papers
Visual Bias in Simulated Users: The Impact of Luminance and Contrast on Reinforcement Learning-based Interaction
Hannah Selder, Charlotte Beylier, Nico Scherf +1
Reinforcement learning (RL) enables simulations of HCI tasks, yet their validity is questionable when performance is driven by visual rendering artifacts distinct from interaction…
Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning
Charlotte Beylier, Hannah Selder, Arthur Fleig +2
While deep reinforcement learning agents demonstrate high performance across domains, their internal decision processes remain difficult to interpret when evaluated only through pe…
Curvature as a tool for evaluating dimensionality reduction and estimating intrinsic dimension
Charlotte Beylier, Parvaneh Joharinad, Jürgen Jost +1
Utilizing recently developed abstract notions of sectional curvature, we introduce a method for constructing a curvature-based geometric profile of discrete metric spaces. The curv…
Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics
Charlotte Beylier, Simon M. Hofmann, Nico Scherf
The learning process of a reinforcement learning (RL) agent remains poorly understood beyond the mathematical formulation of its learning algorithm. To address this gap, we introdu…