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
· Annual Conference Computatio... · 0 citations