Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output\n Feedback Systems
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".