Researchers have developed a new method for approximating Gaussian distributions in entropy-regularized Q-learning with function approximation. The study establishes convergence rates for averaged iterates generated by asynchronous Q-learning, achieving a Gaussian approximation bound with a rate of order n^{-1/4}. This work combines linearization of the soft Bellman recursion with a Gaussian approximation for the leading martingale term, also deriving high-order moment bounds for the algorithm's final iterate. AI
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IMPACT Establishes theoretical bounds for Q-learning algorithms, potentially improving sample efficiency in reinforcement learning applications.
RANK_REASON The cluster contains an academic paper detailing a new theoretical result in machine learning.
