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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Geometric tempering yields exponential convergence bounds for both Wasserstein and Fisher-Rao flows but produces no speedup in the Fisher-Rao metric, with new adaptive schedules derived from the tempered dynamics.
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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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Properties and limitations of geometric tempering for gradient flow dynamics
Geometric tempering yields exponential convergence bounds for both Wasserstein and Fisher-Rao flows but produces no speedup in the Fisher-Rao metric, with new adaptive schedules derived from the tempered dynamics.