Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning

Abstract:We prove a non-asymptotic central limit theorem for vector-valued martingale differences using Stein's method, and use Poisson's equation to extend the result to functions of Markov Chains. We then show that these results can be applied to establish a non-asymptotic central limit theorem for Temporal Difference (TD) learning with averaging.

Probability (math.PR); Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC)

arXiv:2401.15719 [math.PR]

(or arXiv:2401.15719v5 [math.PR] for this version)

https://doi.org/10.48550/arXiv.2401.15719 Focus to learn more arXiv-issued DOI via DataCite

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