A Twin-Delayed Deep Deterministic Policy Gradient Approach for UAV Formation Control
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Bibliographic record
Abstract
This paper explores the use of a Twin-Delayed Deep Deterministic Policy Gradient (TD3) approach for the formation control of multiple Uncrewed Aerial Vehicles (multi-UAVs). A leader-follower configuration is adopted. The proposed TD3 algorithm integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with the double <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q-\mathbf{learning}$</tex> technique, creating a continuous controller for the formation tracking of multi-UAVs. Unlike the DDPG, TD3 utilizes two <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q-\mathbf{networks}$</tex> that explore the environment independently, selecting the smaller <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q-\mathbf{values}$</tex> to compute targets and update the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q-\mathbf{functions}$</tex>. Additionally, the target action policy is constrained within a valid action range, and updates are deliberately delayed to prevent the exploitation of erroneous experiences. Simulation results validate the effectiveness of the proposed method.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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