On Meeting a Maximum Delay Constraint Using Reinforcement Learning
Bibliographic record
Abstract
Several emerging applications in wireless communications are required to achieve low latency, but also high traffic rates and reliabilities. From a latency point of view, most of the state-of-the-art techniques consider the average latency which may not directly apply to scenarios with stringent latency constraints. In this paper, we consider scheduling under a max-delay constraint; this is an NP-hard problem. We propose a novel approach to tackle the scheduling problem by directly addressing the constraint. We consider the downlink of a multi-cell wireless communication network with nodes communicating with users each facing their own delay constraint on randomly arrived packets. Packets must be scheduled to meet the users’ delay constraints. Our main contributions are first, proposing a new search approach, Super State Monte-Carlo Tree Search (SS-MCTS), as a version of regular MCTS modified for large-scale probabilistic environments; second, developing trained value and policy networks to reduce computational complexity, and finally, addressing the scheduling problem through a reinforcement learning framework. Our numerical results demonstrate that the proposed approach significantly improves the packet delivery rate over a baseline approach while meeting the max-delay constraint, and addressing the scalability as the main issues in large action-state spaces.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".