Structured Online Learning‐Based Control of Continuous‐Time Nonlinear Systems
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Bibliographic record
Abstract
This chapter introduces a Model-based Reinforcement Learning technique for control of nonlinear continuous-time systems with unknown dynamics. It formulates an optimal control approach based on a particular structure of dynamics and characterize the optimal feedback control based on a matrix of parameters obtained by a differential equation. The chapter outlines the Structured Online Learning (SOL) algorithm designed based on the obtained results. It then presents the numerical results of this algorithm implemented on a few benchmark examples. The chapter also presents the stability analysis of the approach and its connections with the Forward-Propagating Riccati Equation for linear systems. It discusses the steps involved in more details by focusing on the Sparse Identification of Nonlinear Dynamics algorithm for identification. The chapter compares the results obtained by SOL with other techniques in the literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it