AI RESEARCH
Tight Sample Complexity Bounds for Entropic Best Policy Identification
arXiv CS.LG
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ArXi:2605.13717v1 Announce Type: new We study best-policy identification for finite-horizon risk-sensitive reinforcement learning under the entropic risk measure. Recent work established a constant gap in the exponential horizon dependence between lower and upper bounds on the number of samples required to identify an approximately optimal policy. Precisely, known lower bounds scale in $\Omega(e^{|\beta| H})$ where $H$ is the horizon of the MDP, while the state-of-the-art upper bound achieves at best $O(e^{2|\beta| H})$ (arXi:2506.00286v2) using a generative model.