Deep Reinforcement Learning for Router Selection in Network With Heavy Traffic
Why this work is in the frame
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Bibliographic record
Abstract
The rapid development of wireless communications brings a tremendous increase in the amount number of data streams and poses significant challenges to the traditional routing protocols. In this paper, we leverage deep reinforcement learning (DRL) for router selection in the network with heavy traffic, aiming at reducing the network congestion and the length of the data transmission path. We first illustrate the challenges of the existing routing protocols when the amount of the data explodes. We then utilize the Markov decision process (RSMDP) to formulate the routing problem. Two novel deep Q network (DQN)-based algorithms are designed to reduce the network congestion probability with a short transmission path: one focusing on reducing the congestion probability; while the other focuses on shortening the transmission path. The simulation results demonstrate that the proposed algorithms can achieve higher network throughput comparing to existing routing algorithms in heavy network traffic scenarios.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it