Deep-Reinforcement-Learning-Based Computation Offloading and Power Allocation Within Dynamic Platoon Network
Bibliographic record
Abstract
With the development of Internet of Vehicles (IoV) technology and the application of artificial intelligence-based algorithms, platoon driving based on connected autonomous vehicles (CAVs) has become one of the effective solutions to reduce environmental pollution and improve traffic safety. However, the connectivity, autonomy, and passenger comfort in platooning vehicles cannot be realized without the support of advanced communication technologies and auxiliary computing. In this work, we research the problem of computation offloading and resource allocation within a platoon network. Considering the comprehensive effects of vehicle mobility, co-channel interference, and multivehicle cooperation, we propose a system optimization model for joint computation offloading and power allocation (COPA). Our objective is to minimize the weighted sum of the system average energy consumption and task data processing delay. In the dynamic platoon network, we design a multiagent deep deterministic policy gradient (DDPG)-based joint COPA scheme, which can learn the temporal correlation of environment states and make more accurate power allocation actions. Moreover, we conduct extensive computer simulations to demonstrate the robustness and effectiveness of the DDPG-based COPA scheme. Numerical results demonstrate that the proposed scheme has a better performance compared with other benchmark schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".