Pareto: Fair Congestion Control With Online Reinforcement Learning
Bibliographic record
Abstract
Modern-day computer networks are highly diverse and dynamic, calling for fair and adaptive network congestion control algorithms with the objective of achieving the best possible throughput, latency, and inter-flow fairness. Yet, prevailing congestion control algorithms, such as hand-tuned heuristics or those fueled by deep reinforcement learning agents, may struggle to perform well on multiple diverse networks. Besides, many algorithms are unable to adapt to time-varying real-world networking environments; and some algorithms mistakenly overlooked the need of explicitly taking inter-flow fairness into account, and just measured it as an afterthought. In this paper, we propose a new staged training process to trainPareto, a new congestion control algorithm that generalizes well to a wide variety of environments. Different from existing congestion control algorithms running reinforcement learning agents, Pareto is trained for fairness using the first multi-agent reinforcement learning framework that is communication-free. Pareto continues training online adapting to newly observed environments in the real-world. Our extensive array of experiments shows that Pareto (i) performs well in a wide variety of environments, (ii) offers the best fairness when it comes to competing with other flows sharing the same network link, and (iii) improves its performance with online learning to surpass the state-of-the-art.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".