Online Inference in Distributional Temporal-Difference Learning
The study analyzes online statistical inference for functionals of return distributions estimated via nonparametric distributional temporal-difference learning from a single trajectory. It proves that the root-T error converges to a Gaussian limit, validating bootstrap inference for smooth functionals like variance and CVaR, and develops local asymptotic theory for nonsmooth functionals such as expected shortfall.