Extends robust MDPs to continuous time with policy gradient derivations using differential equation methods and proposes optimizers achieving linear convergence and specific sample complexities.
and Ryu, E
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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.
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Policy Gradient for Continuous-Time Robust Markov Decision Processes
Extends robust MDPs to continuous time with policy gradient derivations using differential equation methods and proposes optimizers achieving linear convergence and specific sample complexities.
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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.