CoGroup: Cooperative Quality Offloading with Worker Grouping using Hierarchical Multi-Agent Deep Reinforcement Learning
Bibliographic record
Abstract
Task offloading in Vehicular Edge Computing (VEC) enhances cooperative perception (CP) for Autonomous Vehicles (AVs), improving traffic awareness. However, the high cost of Roadside Unit (RSU) and the underutilization of parked vehicles pose challenges. Leveraging Vehicle-to-Vehicle (V2V) communication, parked vehicles can form collaborative worker groups for efficient perception aggregation. We propose CoGroup, a two-tier framework integrating task offloading and dynamic worker grouping. Modeled as a double quadratic multiple knapsack problem, it employs Hierarchical Reinforcement Learning (HRL): QMIX for decentralized task allocation and DQN for optimized worker grouping. Experiments show that CoGroup improves traffic awareness by 21% over non-cooperative methods, reducing RSU dependence and offering a scalable, cost-effective solution for next-generation VEC systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".