# A Hybrid Framework of Reinforcement Learning and Convex Optimization for UAV-Based Autonomous Metaverse Data Collection

arXiv: https://arxiv.org/abs/2305.18481 · HTML: https://mlsanity.com/paper/2305.18481/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [A Theoretical Analysis of Deep Q-Learning](https://mlsanity.com/paper/1901.00137/) (2019)
- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [Oracle Complexity of Second-Order Methods for Smooth Convex Optimization](https://mlsanity.com/paper/1705.07260/) (2017)
- [Will Metaverse be NextG Internet? Vision, Hype, and Reality](https://mlsanity.com/paper/2201.12894/) (2022)
- [A Dynamic Resource Allocation Framework for Synchronizing Metaverse with IoT Service and Data](https://mlsanity.com/paper/2111.00431/) (2021)
- [UAV Assisted Data Collection for Internet of Things: A Survey](https://mlsanity.com/paper/2211.09555/) (2022)
- [Towards Big data processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System](https://mlsanity.com/paper/1906.05023/) (2019)

## Built on by (most cited first, 2 of 2)

- [Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network](https://mlsanity.com/paper/2310.09299/) (2023)
- [Research on environment perception and behavior prediction of intelligent UAV based on semantic communication](https://mlsanity.com/paper/2501.04480/) (2025)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT). This paper considers a UAV-assisted Metaverse network, in which UAVs extend the coverage of the base station (BS) to collect the Metaverse data generated at roadside units (RSUs). Specifically, to improve the data collection efficiency, resource allocation and trajectory control are integrated into the system model. The time-dependent nature of the optimization problem makes it non-trivial to be solved by traditional convex optimization methods. Based on the proposed UAV-assisted Metaverse network system model, we design a hybrid framework with reinforcement learning and convex optimization to {cooperatively} solve the time-sequential optimization problem. Simulation results show that the proposed framework is able to reduce the mission completion time with a given transmission power resource.