# Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle

arXiv: https://arxiv.org/abs/2202.08972 · HTML: https://mlsanity.com/paper/2202.08972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management](https://mlsanity.com/paper/1801.05500/) (2018)
- [Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocol](https://mlsanity.com/paper/1809.02934/) (2018)
- [UAV Communications Based on Non-Orthogonal Multiple Access](https://mlsanity.com/paper/1809.05767/) (2018)

## Abstract (arXiv)

As intelligent transportation systems been implemented broadly and unmanned arial vehicles (UAVs) can assist terrestrial base stations acting as multi-access edge computing (MEC) to provide a better wireless network communication for Internet of Vehicles (IoVs), we propose a UAVs-assisted approach to help provide a better wireless network service retaining the maximum Quality of Experience(QoE) of the IoVs on the lane. In the paper, we present a Multi-Agent Graph Convolutional Deep Reinforcement Learning (M-AGCDRL) algorithm which combines local observations of each agent with a low-resolution global map as input to learn a policy for each agent. The agents can share their information with others in graph attention networks, resulting in an effective joint policy. Simulation results show that the M-AGCDRL method enables a better QoE of IoTs and achieves good performance.