A-MARL: Agile Multi-Agent Reinforcement Learning for Soft Real-Time Task Scheduling in Edge Computing
Bibliographic record
Abstract
Modern soft real-time applications (SRTAs) impose heavy computational demands on embedded devices. While offloading workloads to Edge Computing (EC) resources is attractive, task scheduling remains challenging due to strict timing constraints, a vast search space, multiple conflicting objectives, and highly dynamic environments. Conventional heuristic and meta-heuristic algorithms struggle to adapt to these conditions. Although reinforcement learning (RL) suits dynamic environments, single-agent RL converges slowly on mediumand large-scale problems due to enormous action spaces and excessive exploration. We present Agile Multi-Agent Reinforcement Learning (A-MARL), which enhances Multi-Agent PPO by replacing conventional exploration with entropy-guided rule-based exploration. When policy entropy is high, A-MARL employs Shortest Processing Time (SPT) to guide exploration toward promising action space regions. This adaptive mechanism accelerates convergence and delivers schedules better suited for SRTAs in EC environments. Experiments on representative scenarios show A-MARL consistently outperforms state-of-the-art baselines across all evaluated metrics, demonstrating its effectiveness for SRTA task scheduling in EC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".