Natural policy gradient is a special case of doubly smoothed policy iteration that achieves distribution-free global geometric convergence to an epsilon-optimal policy in O((1-gamma)^{-1} log((1-gamma)^{-1} epsilon^{-1})) iterations.
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Establishes Õ(1/k) mean-square last-iterate convergence for asynchronous average-reward Q-learning with adaptive stepsizes and proves adaptivity is necessary.
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Natural Policy Gradient as Doubly Smoothed Policy Iteration: A Bellman-Operator Framework
Natural policy gradient is a special case of doubly smoothed policy iteration that achieves distribution-free global geometric convergence to an epsilon-optimal policy in O((1-gamma)^{-1} log((1-gamma)^{-1} epsilon^{-1})) iterations.
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From Set Convergence to Pointwise Convergence: Finite-Time Guarantees for Average-Reward Q-Learning with Adaptive Stepsizes
Establishes Õ(1/k) mean-square last-iterate convergence for asynchronous average-reward Q-learning with adaptive stepsizes and proves adaptivity is necessary.