Model-Free Adaptive Control Approach Using Integral Reinforcement Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integral reinforcement learning control approaches with derivative weighting performance indices require full knowledge of dynamic models of the considered systems. These approaches do not provide straightforward solutions for underlying integral Bellman optimality equations. This urged for innovative online model-free processes with simple adaptation mechanisms. An online integral reinforcement learning control approach is developed herein for systems operating in uncertain dynamical environments. It employs a value iteration adaptation process to solve the underlying integral temporal difference equation accompanied by model-free optimal control strategies. The proposed approach is tested to control a flexible wing aircraft where the system dynamics are not required by the online learning process. The stability and convergence properties of the adaptive learning mechanism are formally proven before they are validated through numerical simulations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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