von Mises-Fisher Sampling of GloVe Vectors
Walid Bendada · Guillaume Salha-Galvan · Romain Hennequin · Théo Bontempelli · Thomas Bouabça · Tristan Cazenave
Abstract
A recent publication introduced von Mises-Fisher exploration (vMF-exp), a scalable sampling method for exploring large action sets in reinforcement learning problems where hyperspherical embedding vectors represent these actions. We present the first experimental validation of vMF-exp’s key theoretical and scalability properties on a publicly available real-world dataset, confirming the potential of this method.
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