Author

M. Bagatella

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#machine learning Preprint Sep 2026

Deep Epistemic Value Functions for Optimistic Exploration

A systematic empirical study of how epistemic uncertainty is represented, propagated, and optimized in deep epistemic value functions, and uncover distinct failure modes along each of these axes provides evidence that deep epistemic value functions are a promising path toward scalable, principled exploration.

Leander Diaz-Bone, M. Bagatella, Jonas Hübotter et al. · 0 citations

#machine learning Preprint Aug 2026

Three Steps at a Time: Learning Representations from Action Sequences in Contrastive RL

This work extends contrastive reinforcement learning (CRL), a prototypical self-supervised method, to operate over action chunks, and finds that this results in large, pervasive gains across established offline and online benchmarks: +31.7% and +93.1% across 18 and 11 environments respectively.

Michal Korniak, Kamil Dybek, Benjamin Eysenbach et al. · 3 citations · ⚡1