Real NVP uses affine coupling layers to create invertible transformations that support exact density estimation, sampling, and latent inference without approximations.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
3 Pith papers cite this work. Polarity classification is still indexing.
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DreamerV3 uses world models and robustness techniques to solve over 150 tasks across domains with a single configuration, including Minecraft diamond collection from scratch.
A framework for baseline-corrected values in EFGs is introduced, with new baselines including a predictive one that is provably optimal for zero-variance estimates under certain sampling schemes.
citing papers explorer
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Density estimation using Real NVP
Real NVP uses affine coupling layers to create invertible transformations that support exact density estimation, sampling, and latent inference without approximations.
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Mastering Diverse Domains through World Models
DreamerV3 uses world models and robustness techniques to solve over 150 tasks across domains with a single configuration, including Minecraft diamond collection from scratch.
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Low-Variance and Zero-Variance Baselines for Extensive-Form Games
A framework for baseline-corrected values in EFGs is introduced, with new baselines including a predictive one that is provably optimal for zero-variance estimates under certain sampling schemes.