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Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback Marcel T orne 1,2 Max Balsells 3 Zihan Wang
This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.
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PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
Standard approaches for uncertainty quantification in deep learning and physics-informed learning have persistent limitations.
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