Reinforcement Learning-based Hybrid Routing Algorithms for Vehicular Ad Hoc Networks
Bibliographic record
Abstract
This paper proposes a new hybrid routing algorithm, ReLeVR, for vehicular ad hoc networks (VANETs). Our algorithm aims to efficiently solve the problem of routing messages from vehicles to specific geographical locations over VANETs. We assume that a VANET consists of vehicles and roadside units (RSUs). First, we propose a simple strategy to find the optimal locations for RSUs with the objective of minimizing their number. Our strategy computes RSU locations using available prior traffic information. Then we use Q-learning, a reinforcement learning algorithm, to learn from traffic flow patterns and compute a routing policy for the vehicles. This policy determines which grid the message should be sent to in the next step along the way for each location in the city. The routing policy is broadcast to all of the vehicles in the VANET. We demonstrate through simulations on real traffic data that our algorithm outperforms an older algorithm GPSR and a recent Q-learning-based algorithm, QGrid G in terms of metrics like the delivery ratio and delay. We observe that the number of RSUs used by our algorithm is significantly lower than that of a recent algorithm, QTAR.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".