aiqbench analysis
This research advances distributional reinforcement learning by establishing functional central limit theorems and enabling efficient online statistical inference without storing full trajectory data. The method reduces memory overhead while maintaining asymptotic validity, offering a practical tool for analyzing convergence in complex RL environments where generative models are available. It represents a significant theoretical step toward robust uncertainty quantification within deep reinforcement learning frameworks.
Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
arXiv:2608.12973v2 Announce Type: replace-cross Abstract: In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning. Assuming access to a generative model, we first establish functional central limit theorems for both synchronous and asynchronous QTD, which show that the averaged iterates of QTD converge weakly to a rescaled Brownian motion. We next provide online inference methods. Based on random scaling, the inference procedure constructs an asymptotically pivotal statistic for inference by using the information along the whole QTD path. Meanwhile, the proposed statistic can be computed online without storing the entire trajectory of QTD iterates. This substantially reduces the memory requirement and enables efficient statistical inference in distributional reinforcement learning.
Abstract:In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning. Assuming access to a generative model, we first establish functional central limit theorems for both synchronous and asynchronous QTD, which show that the averaged iterates of QTD converge weakly to a rescaled Brownian motion. We next provide online inference methods. Based on random scaling, the inference procedure constructs an asymptotically pivotal statistic for inference by using the information along the whole QTD path. Meanwhile, the proposed statistic can be computed online without storing the entire trajectory of QTD iterates. This substantially reduces the memory requirement and enables efficient statistical inference in distributional reinforcement learning.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2608.12973 [stat.ML]
(or arXiv:2608.12973v2 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2608.12973 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 209 words
Related coverage
Online Inference in Distributional Temporal-Difference Learning
arXiv · Aug 17, 2026
Presentation: Adaptive Recommenders in the Real World: Inference, Evals, and System Design
InfoQ · Sep 26, 2026
What's Continual Learning, and Why Might We Expect To See It In Advanced LLM Agents?
LessWrong · Jun 12, 2026
Enabling Language-specific Reasoning in Multilingual Models with Reinforcement Learning
AMD · Jul 24, 2026