AI RESEARCH
Gaussian Approximation for Asynchronous Q-learning
arXiv CS.LG
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ArXi:2604.07323v1 Announce Type: cross In this paper, we derive rates of convergence in the high-dimensional central limit theorem for Polyak-Ruppert averaged iterates generated by the asynchronous Q-learning algorithm with a polynomial stepsize $k^{-\omega},\, \omega \in (1/2, 1