# Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2001.10268 · HTML: https://mlsanity.com/paper/2001.10268/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 21 of 21)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [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)
- [UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms](https://mlsanity.com/paper/2211.14931/) (2022)
- [Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2409.01092/) (2024)
- [Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition](https://mlsanity.com/paper/2501.08007/) (2025)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning](https://mlsanity.com/paper/2102.02078/) (2021)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Dynamic Trajectory and Power Control in Ultra-Dense UAV Networks: A Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2411.14052/) (2024)
- [Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices](https://mlsanity.com/paper/2501.15305/) (2025)
- [Conditional Diffusion Model-Driven Generative Channels for Double RIS-Aided Wireless Systems](https://mlsanity.com/paper/2506.12682/) (2025)
- [Multi-Agent Path Planning based on MPC and DDPG](https://mlsanity.com/paper/2102.13283/) (2021)
- [Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2203.14152/) (2022)
- [AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing](https://mlsanity.com/paper/2510.23053/) (2025)
- [Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint](https://mlsanity.com/paper/2109.05767/) (2021)
- [Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning](https://mlsanity.com/paper/2309.12534/) (2023)
- [Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network](https://mlsanity.com/paper/2408.14298/) (2024)
- [Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT](https://mlsanity.com/paper/2409.15711/) (2024)
- [Learning-Based Dynamic Obstacle Avoidance for a UAV Using Only Three Range Sensors](https://mlsanity.com/paper/2609.14426/) (2026)

## Abstract (arXiv)

In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing network, where the UAV executes computational tasks offloaded from mobile terminal users (TUs) and the motion of each TU follows a Gauss-Markov random model. To ensure the quality-of-service (QoS) of each TU, the UAV with limited energy dynamically plans its trajectory according to the locations of mobile TUs. Towards this end, we formulate the problem as a Markov decision process, wherein the UAV trajectory and UAV-TU association are modeled as the parameters to be optimized. To maximize the system reward and meet the QoS constraint, we develop a QoS-based action selection policy in the proposed algorithm based on double deep Q-network. Simulations show that the proposed algorithm converges more quickly and achieves a higher sum throughput than conventional algorithms.