High-order generator regression from multi-step trajectories yields a second-order accurate estimator for finite-horizon continuous-time policy evaluation that outperforms the Bellman baseline in calibration studies and benchmarks.
IEEE Transactions on Automatic Control , volume=
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New Berry-Esseen bounds for multivariate martingale difference sequences achieve n^{-1/4} rate and polylog(d) dimension dependence in Kolmogorov distance.
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Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation
High-order generator regression from multi-step trajectories yields a second-order accurate estimator for finite-horizon continuous-time policy evaluation that outperforms the Bellman baseline in calibration studies and benchmarks.
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Berry-Esseen bounds for multivariate martingale difference sequences in the Kolmogorov distance
New Berry-Esseen bounds for multivariate martingale difference sequences achieve n^{-1/4} rate and polylog(d) dimension dependence in Kolmogorov distance.