arXiv Machine Learning
Jul 28

Finite-Time Analysis of the Natural Policy Gradient in Finite-Horizon Markov Decision Processes

arXiv:2607. 22982v1 Announce Type: new Abstract: Natural Policy Gradient (NPG) is a well-established Reinforcement Learning algorithm that underlies widely used methods such as Trust Region Policy Optimization and Proximal Policy Optimization, both of which have demonstrated strong empirical success.

By Asha Barua, Sajad Khodadadian
arXiv Machine Learning
Sep 23

Tight Sample Complexity Bounds for Entropic Best Policy Identification

The paper investigates best‑policy identification in finite‑horizon, risk‑sensitive reinforcement learning using the entropic risk measure. It identifies a gap between known lower bounds ≥ η(e^{|eta|H}) and upper bounds ≤ O(e^{2|eta|H}) for sample complexity, attributing the excess factor to loose concentration bounds for exponential utilities. By employing a forward‑model algorithm with KL‑based exploration bonuses and a novel stopping rule, the authors achieve a sample complexity that matches the lower bound, closing the previously open exponential gap.

By Amer Essakine, Claire Vernade