Spatial-Temporal Graph Attention-Based Multi-Agent Reinforcement Learning in Cooperative Edge Caching
Bibliographic record
Abstract
With the increasing number of users connecting to the internet and the number of devices each user owns, the internet is experiencing an unprecedented traffic demand. Providing users with a satisfying surfing experience while consuming minimal transmission costs is critical but also challenging. To cope with these difficulties, edge caching is emerging. Edge caching allows Base Stations (BSs) to cache files, then some of the users' requests can be satisfied by the edge rather than the cloud, where the latter results in higher latency and transmission costs. However, state-of-the-art edge caching strategies either assume file popularity is known in advance or lack of cooperation between neighbouring BSs. This paper proposes a multi-agent spatial-temporal graph attention neural network caching strategy, named “Double Deep Graph Attention Recurrent Q Network” (DDGARQN). The graph attention block can extract spatial dependencies among neighbouring BSs, and the temporal block can capture user preferences dynamics on each Base Station (BS) at each time instant. Comprehensive experimental results show that DDGARQN can achieve a 66% higher cache hit ratio, 6.9% lower latency and 6.5% lower link load than the state-of-the-art caching strategy at best.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".