Counterfactual Multi-Agent DRL for Efficient Task Offloading in Vehicular Edge Computing
Bibliographic record
Abstract
This paper addresses the problem of task offloading and scheduling in Vehicular Edge Computing (VEC) by incorporating dynamic task prioritization across distributed zones. The objective is to maximize task completion within deadline constraints while minimizing latency and energy consumption. To this end, the problem is formulated as a Partially Ob servable Markov Decision Process (POMDP), and solved using a Counterfactual Multi-Agent (COMA) reinforcement learning framework with centralized training and decentralized execution. The proposed approach enables distributed agents to jointly determine task priority classes, edge server associations, and CPU frequencies, leveraging counterfactual credit assignment to coordinate decision-making in both cooperative and competitive environments. Simulation results demonstrate that COMA achieves zone-aware task prioritization with improved fairness and resource distribution. It matches or exceeds the performance of centralized value-based baselines and reduces average latency by approximately 6% compared to centralized Deep Q-Learning. Additionally, it offers more balanced prioritization than multi agent deterministic policy gradient methods, highlighting its effectiveness in dynamic, distributed edge environments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".