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Near Minimax Rate for Quantile Distributional Reinforcement Learning

arXiv:2610.29595v1 Announce Type: new Abstract: In this paper, we study quantile distributional policy evaluation, where the return distribution under a given policy is approximated by the quantile Bellman fixed point $\bm\eta_m$. We investigate the statistical accuracy of estimating $\bm\eta_m$ ac…

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  1. 2026-10-22 04:00 · arXiv stat.ML
    Near Minimax Rate for Quantile Distributional Reinforcement Learning

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