# Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing

arXiv: https://arxiv.org/abs/1911.03887 · HTML: https://mlsanity.com/paper/1911.03887/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2019.

## Built on by (most cited first, 16 of 16)

- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system](https://mlsanity.com/paper/2408.01248/) (2024)
- [Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments](https://mlsanity.com/paper/2207.01251/) (2022)
- [Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2505.03230/) (2025)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks](https://mlsanity.com/paper/2501.14603/) (2025)
- [Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices](https://mlsanity.com/paper/2501.15305/) (2025)
- [Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations](https://mlsanity.com/paper/2312.10490/) (2023)
- [Tackling Variabilities in Autonomous Driving](https://mlsanity.com/paper/2104.10415/) (2021)
- [On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2312.07917/) (2023)
- [Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning](https://mlsanity.com/paper/2403.10761/) (2024)
- [Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2508.06863/) (2025)
- [Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction](https://mlsanity.com/paper/2512.20902/) (2025)
- [Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration](https://mlsanity.com/paper/2603.03595/) (2026)
- [PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks](https://mlsanity.com/paper/2607.17922/) (2026)
- [DSWM: Decomposed Spatio-Temporal World Model for Demand-Driven UAV Base Station Repositioning](https://mlsanity.com/paper/2609.36845/) (2026)

## Abstract (arXiv)

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all the UEs via optimizing the user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based Trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the similar performance and both outperform traditional algorithms.