A Double Q-Learning Approach for Navigation of Aerial Vehicles with\n Connectivity Constraint
Bibliographic record
Abstract
This paper studies the trajectory optimization problem for an aerial vehicle\nwith the mission of flying between a pair of given initial and final locations.\nThe objective is to minimize the travel time of the aerial vehicle ensuring\nthat the communication connectivity constraint required for the safe operation\nof the aerial vehicle is satisfied. We consider two different criteria for the\nconnectivity constraint of the aerial vehicle which leads to two different\nscenarios. In the first scenario, we assume that the maximum continuous time\nduration that the aerial vehicle is out of the coverage of the ground base\nstations (GBSs) is limited to a given threshold. In the second scenario,\nhowever, we assume that the total time periods that the aerial vehicle is not\ncovered by the GBSs is restricted. Based on these two constraints, we formulate\ntwo trajectory optimization problems. To solve these non-convex problems, we\nuse an approach based on the double Q-learning method which is a model-free\nreinforcement learning technique and unlike the existing algorithms does not\nneed perfect knowledge of the environment. Moreover, in contrast to the\nwell-known Q-learning technique, our double Q-learning algorithm does not\nsuffer from the over-estimation issue. Simulation results show that although\nour algorithm does not require prior information of the environment, it works\nwell and shows near optimal performance.\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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".