A Decentralized Cooperative Coverage Control for Networked Multiple UAVs Based on Deep Reinforcement Learning
Bibliographic record
Abstract
Deployment of the unarmed aerial vehicle (UAV) swarm promises increased efficiency and safety of area search coverage. Multiple UAVs must be capable of autonomous collaboration and area search coverage for this. Therefore, we integrate multi-agent reinforcement learning into the cooperative control method of UAV swarm and propose a decentralized cooperative control for networked multiple UAVs technique based on the extended-proximal policy optimization algorithm (EPPO). The proposed approach not only adopts distributed training for multiple agents, but also allows them to obtain some mutual state information, such as position information and searched sub-areas. So, it can significantly speed up training and increase the effectiveness and safety of task completion in real-world applications. After a simulation, the multi-intelligent UAV can rapidly cover 100% of the mission area and ensure more excellent safety.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".