Attentional Communication for Multi-Agent Distributed Resource Allocation in V2X Networks
Bibliographic record
Abstract
Cooperative multi-agent reinforcement learning (MARL) is a promising solution for many large-scale multi-agent system (MAS) scenarios. A MARL framework is usually based on a decentralized scheme that enables communication between all agents in a given architecture. The agents exchange information to maximize their average reward and increase the overall system performance. However, this decentralized information sharing results in high communication costs, which is a critical issue for environments with limited communication bandwidth. On the other hand, a predefined inter-agent communication architecture may limit potential cooperation. This paper addresses such issues in a vehicle-to-everything (V2X) network, a typical example of MAS with strict Quality of Service (QoS) requirements. For efficient utilization of limited network resources, a solution to the resource-sharing problem between Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) links is required. We propose a POST-Attentional Communication Actor-Critic (POST-2AC) model that learns when communication is needed and how to integrate shared information for cooperative decision-making. Our learning method uses an attention approach combined with the critic-network to label the agents local information based on its importance so that each agent learns to trade off its performance and communication cost. The simulation results show that the proposed model achieves better performance than the state-of-the-art baselines.
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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 | 0.001 | 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".