Pitch Control of an Aircraft with Aggregated Reinforcement Learning Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Pitch control is a basic function of an Automatic Flight Control System (AFCS). Due to the complexity of problems, stochastic behavior, and the disturbing of the environment, traditional techniques, such as, linear feedback control, quantitative feedback theory, and adaptive control, which are all based on the explicit aerodynamic model of an aircraft, are not efficient in designing pitch controllers. This paper adopts multiple Reinforcement Learning (RL) algorithms and Cerebellar Model Articulation Controller (CMAC) techniques to design a pitch controller. In order to improve learning and control performances, a learn system named "Aggregated Multiple Reinforcement Learning System (AMRLS)" is proposed, which combines the outcomes of individual RL algorithms by using several aggregation methods. The goal of this paper is to demonstrate that the improved RL based control technology can be applied effectively to pitch control problem.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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