Multi-Agent Actor-Critic for Cooperative Resource Allocation in Vehicular Networks
Why this work is in the frame
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Bibliographic record
Abstract
The rapid evolution of vehicular communication has enabled new services that facilitate the road experience for drivers and passengers. The scarcity of the network resources and the different Quality of Service (QoS) demands of the offered services have stressed the need for a cooperative resource allocation scheme between Vehicle to Infrastructure (V2I) links and Vehicle to Vehicle (V2V) links. Therefore, we model this re-source allocation problem as a multi-agent reinforcement learning (MARL) problem, then we design a MARL solution by proposing a cooperative advantage actor-critic (A2C) approach with two variants, including Shared-Critic-Shared-Reward (SCSR) and Non-Shared-Critic-Shared-Reward (NSCSR). The performance of both methods is validated by experiments, and the results show that the SCSR has better network entropy and thus better environment exploration, which in turn produces more robust agents able to perform better in new situations.
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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 | 0.001 | 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 it