A MARL Approach for Finding Optimal Positions for VANET Aerial Base-stations on a Sparse Highway
Bibliographic record
Abstract
A Vehicular Ad-Hoc Network (VANET) helps connected vehicles send and receive environmental and traffic information, making it a crucial component towards fully autonomous roads. For VANETs to serve their purpose, there has to be sufficient coverage, even in areas where there is less demand. Moreover, a lot of the safety information is time-sensitive; excessive outage time in a vehicular network can increase the risk of fatal accidents. Unmanned Aerial Vehicles (UAVs) can be used as mobile base-stations to fill in gaps of coverage. My work is focused on the placement of mobile base-stations for rural highways with sparse traffic, as it represents the worst-case scenario for vehicular communication. The goal is to maximize the segments of road that satisfy a particular communication outage time constraint. I use Multi-Agent Reinforcement Learning (MARL) to learn the optimal placement strategy. The main benefit of MARL is that it allows the agents to learn complex strategies through experience. I propose a variation of the traditional Deep Independent Q-Learning. The modifications include an observation function augmented with information directly shared between neighbouring agents as well a shared policy scheme. I also implement a lightweight custom sparse highway simulator that is used for training and testing my algorithm. The experiments show that the proposed MARL algorithm is able to learn the placement policies that produce the maximum rewards for different scenarios while adapting to the dynamic road densities along the highway segment. The experiments also show that the model is scalable, allowing the number of agents to increase without any modifications to the code. The model also displays robustness as it is still able to resume function even after multiple single and dual-point failures. Finally, I show that the model can be generalized as the algorithm can be directly used, with similar performance, on an industry standard simulator. Future experiments can be performed to improve the realism and complexity of the highway models as well as to test the method on real-world data.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".