AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
Vladimir Vovk, Alex Gammerman, and Glenn Shafer.Algorithmic learning in a random world
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A new sub-Riemannian snake model on the projective line bundle uses a symmetric cusp-free pseudo-distance with triangle inequality properties and connected-component costs to enable efficient robust segmentation of overlapping objects in SEM images.
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Planner-Admissible Graph-PDE Value Extensions for Sparse Goal-Conditioned Planning
AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
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Sub-Riemannian Snakes on the Projective Line Bundle with Applications to Segmentation of SEM Images
A new sub-Riemannian snake model on the projective line bundle uses a symmetric cusp-free pseudo-distance with triangle inequality properties and connected-component costs to enable efficient robust segmentation of overlapping objects in SEM images.