Laurin Thiele

Adaptive Rollout Truncation Based on Epistemic Uncertainty for Efficient Offline World Model Training

The proposed epistemic uncertainty-driven adaptive rollout strategy for offline world model training following an auto-curriculum training scheme indicates that epistemic uncertainty is useful not only for downstream policy regularization, but also for making world model training itself more compute-efficient.