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Withdrawn paper details novel continuous-time policy evaluation method

By PulseAugur Editorial · [1 sources] ·

A research paper, now withdrawn, proposed a novel method for continuous-time policy evaluation called High-Order Generator Regression. This technique aims to improve upon the standard Bellman baseline by using multi-step transitions and moment-matching coefficients to estimate the time-dependent generator. The paper theoretically decomposed the estimation error and provided a regime map for when higher-order gains are expected, demonstrating consistent improvements over the Bellman baseline in calibration studies. AI

IMPACT This research explores advanced techniques for policy evaluation, potentially impacting reinforcement learning applications.

RANK_REASON The cluster contains a withdrawn academic paper detailing a novel statistical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Withdrawn paper details novel continuous-time policy evaluation method

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The cluster contains a withdrawn academic paper detailing a novel statistical method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yaowei Zheng, Richong Zhang, Shenxi Wu, Shirui Bian, Haosong Zhang, Li Zeng, Xingjian Ma, Yichi Zhang ·

    Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation

    arXiv:2604.18972v2 Announce Type: replace Abstract: We study finite-horizon continuous-time policy evaluation from discrete closed-loop trajectories under time-inhomogeneous dynamics. The target value surface solves a backward parabolic equation, but the Bellman baseline obtained…

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