Deep Reinforcement Learning Enabled Reverse Offloading in Cooperative Vehicle Edge Computing
Bibliographic record
Abstract
The advent of cooperative vehicle-infrastructure system (CVIS) has the potential to turn the futuristic transportation system into reality. Although CVIS can increase the road safety and traffic efficiency, it also brings the challenge of huge data processing demand. Fortunately, with reverse offloading, the soaring computing capabilities of connected autonomous vehicles (CAV s) can be utilized to address the limitation in computing power. In this paper, the reverse offloading strategy is adopted in a vehicular edge computing (VEC) network, aiming to reduce the system latency through intelligent task partition and resource allocation. Specifically, we firstly formulate the reverse offloading problem as a Markov decision process (MDP), considering the time-varying status of channel quality, task queueing status, and task load. Then, we propose a deep reinforcement learning (DRL) algorithm to learn optimal decisions by interacting with the environment. Specifically, a deep deterministic policy gradient (DDPG) based algorithm is proposed to make task partition and allocation decisions in a constrained continuous manner with the help of Softmax function. Simulation results demonstrate that the proposed approach can significantly reduce the system latency compared to three baseline schemes. Notably, when facing the longer traffic rush time, the advantages of the proposed method are progressively growlng.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".