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Research questionHow can practitioners detect evolving feature reliance in deep reinforcement learning when performance remains adequate?An agent can preserve strong performance while adopting unintended reward shortcuts or overfitting to redundant sensory channels. Performance metrics alone may not reveal these changes in feature reliance.
Machine Learning
Mechanistic Interpretability
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Attention Trajectories as a Diagnostic Axis for Deep Reinforcement LearningThe evidence uses quantitative saliency maps aggregated into object- and modality-level attention profiles, tracked as trajectories during training and related to behavioral measurements. Results cover Atari 2600, custom Pong environments, and biomechanical user simulations in visuomotor tasks, with comparisons across controlled conditions and saliency methods.research paper · Sep 3, 2026
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