# Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network

arXiv: https://arxiv.org/abs/2407.21460 · HTML: https://mlsanity.com/paper/2407.21460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Scalable agent alignment via reward modeling: a research direction](https://mlsanity.com/paper/1811.07871/) (2018)
- [Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward](https://mlsanity.com/paper/2006.06626/) (2020)
- [Adaptive Contention Window Design using Deep Q-learning](https://mlsanity.com/paper/2011.09418/) (2020)
- [The Power of Training: How Different Neural Network Setups Influence the Energy Demand](https://mlsanity.com/paper/2401.01851/) (2024)
- [Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.01492/) (2020)
- [Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning](https://mlsanity.com/paper/2402.14582/) (2024)
- [EdgeMap: CrowdSourcing High Definition Map in Automotive Edge Computing](https://mlsanity.com/paper/2201.07973/) (2022)
- [Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment](https://mlsanity.com/paper/2408.03329/) (2024)

## Built on by (most cited first, 1 of 1)

- [RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet](https://mlsanity.com/paper/2508.09140/) (2025)

## Abstract (arXiv)

Reinforcement Learning (RL) algorithms have been used to address the challenging problems in the offloading process of vehicular ad hoc networks (VANET). More recently, they have been utilized to improve the dissemination of high-definition (HD) Maps. Nevertheless, implementing solutions such as deep Q-learning (DQN) and Actor-critic at the autonomous vehicle (AV) may lead to an increase in the computational load, causing a heavy burden on the computational devices and higher costs. Moreover, their implementation might raise compatibility issues between technologies due to the required modifications to the standards. Therefore, in this paper, we assess the scalability of an application utilizing a Q-learning single-agent solution in a distributed multi-agent environment. This application improves the network performance by taking advantage of a smaller state, and action space whilst using a multi-agent approach. The proposed solution is extensively evaluated with different test cases involving reward function considering individual or overall network performance, number of agents, and centralized and distributed learning comparison. The experimental results demonstrate that the time latencies of our proposed solution conducted in voice, video, HD Map, and best-effort cases have significant improvements, with 40.4%, 36%, 43%, and 12% respectively, compared to the performances with the single-agent approach.