Enhanced DRL Strategy for Distributed Edge Computing in Vehicular Networks
Bibliographic record
Abstract
The rapid growth of connected vehicles has posed significant challenges in managing computational offloading in vehicular networks. However, recent advancements in artificial intelligence, big data, and cloud computing have opened up new possibilities for optimizing computational offloading in vehicular edge computing (VEC) systems. This article addresses the critical and pressing issue of leveraging these new technologies to optimize the allocation of vehicular computational offloading resources at edge servers, with the aim of improving the efficiency of computational offloading services while reducing costs. We focus on the computational offloading problem in a distributed edge computing system, where computational tasks from vehicles can be processed by multiple edge computing resource units (ECRUs). To tackle this problem, we first model the optimization of computational offloading resources as a Markov Decision Process (MDP), taking into account system income, cost expenditure, and other relevant domains. We then propose an Enhanced Deep Q-Learning (EDQL) approach, which leverages reinforcement learning (RL) to solve the optimization problem effectively. Our simulation results demonstrate that the proposed EDQL approach achieves fast convergence speed, high convergence stability, and superior performance compared to existing methods.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".