Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output\n Feedback Systems
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Bibliographic record
Abstract
This paper studies a class of partially observed Linear Quadratic Gaussian\n(LQG) problems with unknown dynamics. We establish an end-to-end sample\ncomplexity bound on learning a robust LQG controller for open-loop stable\nplants. This is achieved using a robust synthesis procedure, where we first\nestimate a model from a single input-output trajectory of finite length,\nidentify an H-infinity bound on the estimation error, and then design a robust\ncontroller using the estimated model and its quantified uncertainty. Our\nsynthesis procedure leverages a recent control tool called Input-Output\nParameterization (IOP) that enables robust controller design using convex\noptimization. For open-loop stable systems, we prove that the LQG performance\ndegrades linearly with respect to the model estimation error using the proposed\nsynthesis procedure. Despite the hidden states in the LQG problem, the achieved\nscaling matches previous results on learning Linear Quadratic Regulator (LQR)\ncontrollers with full state observations.\n
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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