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Amer Essakine

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#machine learning Conference May 2026

Tight Sample Complexity Bounds for Entropic Best Policy Identification

This work revisits the analysis of best-policy identification for finite-horizon risk-sensitive reinforcement learning under the entropic risk measure through a forward-model based algorithm building on KL-based exploration bonuses that adapt to the entropic criterion.

Amer Essakine, Claire Vernade · 0 citations

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