Deep Reinforcement Learning Controller Design for Unmanned Aerial Vehicles
Bibliographic record
Abstract
A Proximal Policy Optimization agent was trained to learn quadrotor dynamics, successfully selecting control outputs to stabilize the drone and track complex trajectories. The agent was trained to mimic a minimum snap trajectory. The UAV closely followed the path, maintaining desired speeds of 3.56 body lengths/second, and remaining within 0.5m of the path, in wind conditions up to 20 mph. The agent was also validated on other complex trajectories, still closely tracking them regardless of the path it was trained on. Compared to PID controllers, the RL controller had a faster response time, converging to the desired path quicker. PID tuning is high maintenance and is limited by linearization around hover state. This results in instabilities and overshoots not observed in the RL controller, as well as RL learning non-linear dynamics. However, the RL controller had noisy motor output, resulting in undesirable oscillatory behaviour not observed in PID.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".