# UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2007.00544 · HTML: https://mlsanity.com/paper/2007.00544/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks](https://mlsanity.com/paper/2207.06131/) (2022)
- [Learning to Recharge: UAV Coverage Path Planning through Deep Reinforcement Learning](https://mlsanity.com/paper/2309.03157/) (2023)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control](https://mlsanity.com/paper/2210.10639/) (2022)
- [Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks](https://mlsanity.com/paper/2104.10403/) (2021)
- [ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting](https://mlsanity.com/paper/2405.11013/) (2024)
- [Autonomous Resource Management in Construction Companies Using Deep Reinforcement Learning Based on IoT](https://mlsanity.com/paper/2208.08087/) (2022)
- [Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2601.14092/) (2026)

## Abstract (arXiv)

Autonomous deployment of unmanned aerial vehicles (UAVs) supporting next-generation communication networks requires efficient trajectory planning methods. We propose a new end-to-end reinforcement learning (RL) approach to UAV-enabled data collection from Internet of Things (IoT) devices in an urban environment. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. While previous approaches, learning and non-learning based, must perform expensive recomputations or relearn a behavior when important scenario parameters such as the number of sensors, sensor positions, or maximum flying time, change, we train a double deep Q-network (DDQN) with combined experience replay to learn a UAV control policy that generalizes over changing scenario parameters. By exploiting a multi-layer map of the environment fed through convolutional network layers to the agent, we show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters that balance the data collection goal with flight time efficiency and safety constraints. Considerable advantages in learning efficiency from using a map centered on the UAV's position over a non-centered map are also illustrated.