Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation
We study finite-horizon continuous-time policy evaluation from discrete closed-loop trajectories under time-inhomogeneous dynamics. The target value surface solves a backward parabolic equation, but the Bellman baseline obtained from one-step recursion is only first-order in the grid width. We estimate the time-dependent generator from multi-step transitions using moment-matching coefficients that cancel lower-order truncation terms, and combine the resulting surrogate with backward regression. The main theory gives an end-to-end decomposition into generator misspecification, projection error, pooling bias, finite-sample error, and start-up error, together with a decision-frequency regime...
| Authors | Yaowei Zheng, Richong Zhang, Shenxi Wu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 21 April 2026 |
| Last updated at source | 7 May 2026 |
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