# Coverage-aware and Reinforcement Learning Using Multi-agent Approach for HD Map QoS in a Realistic Environment

arXiv: https://arxiv.org/abs/2408.03329 · HTML: https://mlsanity.com/paper/2408.03329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [Adaptive Contention Window Design using Deep Q-learning](https://mlsanity.com/paper/2011.09418/) (2020)
- [Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning](https://mlsanity.com/paper/2402.14582/) (2024)

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

- [Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network](https://mlsanity.com/paper/2407.21460/) (2024)

## Abstract (arXiv)

One effective way to optimize the offloading process is by minimizing the transmission time. This is particularly true in a Vehicular Adhoc Network (VANET) where vehicles frequently download and upload High-definition (HD) map data which requires constant updates. This implies that latency and throughput requirements must be guaranteed by the wireless system. To achieve this, adjustable contention windows (CW) allocation strategies in the standard IEEE802.11p have been explored by numerous researchers. Nevertheless, their implementations demand alterations to the existing standard which is not always desirable. To address this issue, we proposed a Q-Learning algorithm that operates at the application layer. Moreover, it could be deployed in any wireless network thereby mitigating the compatibility issues. The solution has demonstrated a better network performance with relatively fewer optimization requirements as compared to the Deep Q Network (DQN) and Actor-Critic algorithms. The same is observed while evaluating the model in a multi-agent setup showing higher performance compared to the single-agent setup.