Communication-Efficient Multi-Agent Actor-Critic Framework for Distributed Optimization of Resource Allocation in V2X Networks
Bibliographic record
Abstract
The vehicular communication technology has enabled new services for drivers and passengers with different Quality of Service (QoS) demands. Due to network resource limitation, a cooperative resource allocation scheme between Vehicle to Infrastructure (V2I) links and Vehicle to Vehicle (V2V) links is needed. In literature, the resource allocation problem can be solved using a cooperative multi-agent reinforcement learning (MARL) framework. Such framework can be implemented in a central controller that receives observations and rewards of all agents, and then calculates the action for each agent accordingly. However, such a central controller may not be realistic for a vehicular network which is highly flexible and requires real-time decision making. Therefore, decentralized schemes where the agents exchange messages to maximize their average rewards would be more appropriate. Nevertheless, decentralized training increases communication costs among the agents, which is a challenging issue for a vehicular network with limited communication bandwidth. This paper proposes an Attentional Double Hierarchical Advantage Actor-Critic (ADHA2C) to address this issue. Specifically, ADHA2C adopts an attention mechanism added to the actor part to classify the important messages sent from other agents in the network. Our extensive experiments and analysis show that the proposed method approximates the performance of the upper bound model, and disturbance in the learning phase can be avoided through our proposed attention mechanism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".