Single-loop actor-critic achieves the first Õ(ε^{-2}) sample complexity for ε-optimal policies under minimal irreducibility assumptions.
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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.
Establishes Õ(1/k) mean-square last-iterate convergence for asynchronous average-reward Q-learning with adaptive stepsizes and proves adaptivity is necessary.
citing papers explorer
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Achieving $\epsilon^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions
Single-loop actor-critic achieves the first Õ(ε^{-2}) sample complexity for ε-optimal policies under minimal irreducibility assumptions.
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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.