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An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks
Temporal difference (TD) learning algorithms with neural network function parameterization have well-established empirical success in many practical large-scale reinforcement learning tasks.
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
Under mild conditions, non-asymptotic finite-sample convergence to the globally optimal Q function is derived for various nonlinear function approximations.
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