Task Migration Strategy in Vehicular Networks Based on Reinforcement Learning
Bibliographic record
Abstract
With the research and application of edge computing in vehicular networks, computing tasks can be offloaded from vehicles to roadside edge servers to reduce system service latency. However, as vehicles move, the computing tasks need to be migrated from one edge server to another. Predicting the vehicles movement trajectory and formulating a reasonable task migration plan for this is a key challenge that needs to be addressed. Traditional computing offloading methods cannot be directly applied in vehicular networks. Therefore, this paper constructs a vehicular task offloading system based on a multi-layered computing network, introduces a Markov mobility model to describe the vehicle movement trajectory, and solves the optimal migration path problem. Since this problem is NP-hard, a solution method based on a constrained Markov model is proposed, along with an Actor-Network Primal-Dual Deep Deterministic Policy Gradient (ANPD-DDPG) algorithm based on reinforcement learning to achieve the optimal solution. Finally, in simulation experiments, the proposed method is compared with existing research, showing about a 33% reduction in system delay and migration cost. The characteristics of the ANPD-DDPG algorithm in terms of convergence speed and system delay are also analyze.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".