New statistical inference methods for temporal-difference learning unveiled

Researchers have developed new methods for online statistical inference concerning the distribution of returns in temporal-difference learning. The study proves that the root-T error of the Polyak--Ruppert averaged estimator converges weakly to a centered Gaussian random element in Cramér space. This work validates bootstrap inference for smooth statistical functionals, such as variance and quantiles, and introduces a local asymptotic theory for nonsmooth functionals. AI

IMPACT Introduces novel statistical inference techniques applicable to temporal-difference learning, potentially improving model accuracy and reliability.

RANK_REASON The cluster contains a research paper published on arXiv detailing new statistical methods for temporal-difference learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• Cramér space
• CVaR
• DagsHub
• Gotit.pub
• Hugging Face
• Polyak--Ruppert
• ScienceCast

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