C-DSAC applies the Cramér distance to distributional value learning inside SAC and outperforms standard SAC on robotic benchmarks, with larger gains in complex environments due to confidence-driven conservative updates.
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FractalPINN-Flow is a fractal-recursive unsupervised network trained with total variation regularization to estimate dense optical flow from image pairs.
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Distributional Reinforcement Learning via the Cram\'er Distance
C-DSAC applies the Cramér distance to distributional value learning inside SAC and outperforms standard SAC on robotic benchmarks, with larger gains in complex environments due to confidence-driven conservative updates.
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FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
FractalPINN-Flow is a fractal-recursive unsupervised network trained with total variation regularization to estimate dense optical flow from image pairs.