arXiv Machine Learning October 19, 2026 By Zijie Cheng, Yang Peng, Zhihua Zhang

Online Inference for Quantile Distributional Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 12973v3 Announce Type: replace-cross Abstract: In this paper, we study online statistical inference for quantile-based distributional policy evaluation, which aims to characterize the distribution of discounted cumulative rewards under a given policy.

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reinforcement-learning

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Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning

arXiv:2608. 12973v1 Announce Type: cross Abstract: In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning.

By Zijie Cheng, Yang Peng, Zhihua Zhang

reinforcement-learning

arXiv Machine Learning

Jul 10

Statistical Efficiency and Inference of Quantile Distributional Reinforcement Learning

arXiv:2607. 08444v1 Announce Type: cross Abstract: In this paper, we study quantile-based distributional reinforcement learning from the perspective of statistical efficiency.

By Zijie Cheng, Yang Peng, Zhihua Zhang

reinforcement-learning

arXiv Machine Learning

Sep 2

Online simultaneous inference for quantiles via smoothed stochastic gradient descent

arXiv:2505.13299v2 Announce Type: replace-cross Abstract: This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the...

By Likai Chen, Georg Keilbar, Wei Biao Wu

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A Finite Sample Analysis for Quantile Temporal Difference Learning in Distributional Reinforcement Learning

We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning. The proof separates two stability mechanisms.

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A Finite-Sample Analysis of Quantile Temporal-Difference Learning

arXiv:2608. 27313v2 Announce Type: replace-cross Abstract: Quantile temporal-difference learning (QTD) is an effective method for learning return distributions through quantile approximation, yet its finite-time behavior remains poorly understood.

By Zijie Cheng, Xiang Li, Yang Peng, Zhihua Zhang

arXiv Machine Learning

Aug 28

A Finite Sample Analysis for Quantile Temporal Difference Learning in Distributional Reinforcement Learning

arXiv:2608. 27313v1 Announce Type: cross Abstract: We establish a global finite-sample guarantee for synchronous quantile temporal-difference learning (QTD) in tabular distributional reinforcement learning.

By Zijie Cheng, Xiang Li, Yang Peng, Zhihua Zhang

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