# Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.12461 · HTML: https://mlsanity.com/paper/2010.12461/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05824/) (2025)
- [Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks](https://mlsanity.com/paper/2306.02029/) (2023)
- [UAV-assisted Internet of Vehicles: A Framework Empowered by Reinforcement Learning and Blockchain](https://mlsanity.com/paper/2502.15713/) (2025)
- [Efficient UAV Trajectory-Planning using Economic Reinforcement Learning](https://mlsanity.com/paper/2103.02676/) (2021)
- [ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting](https://mlsanity.com/paper/2405.11013/) (2024)
- [Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks](https://mlsanity.com/paper/2410.22578/) (2024)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2205.09967/) (2022)
- [Multiscale Adaptive Scheduling and Path-Planning for Power-Constrained UAV-Relays via SMDPs](https://mlsanity.com/paper/2209.07655/) (2022)
- [Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing](https://mlsanity.com/paper/2309.02193/) (2023)
- [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)

Harvesting data from distributed Internet of Things (IoT) devices with multiple autonomous unmanned aerial vehicles (UAVs) is a challenging problem requiring flexible path planning methods. We propose a multi-agent reinforcement learning (MARL) approach that, in contrast to previous work, can adapt to profound changes in the scenario parameters defining the data harvesting mission, such as the number of deployed UAVs, number, position and data amount of IoT devices, or the maximum flying time, without the need to perform expensive recomputations or relearn control policies. We formulate the path planning problem for a cooperative, non-communicating, and homogeneous team of UAVs tasked with maximizing collected data from distributed IoT sensor nodes subject to flying time and collision avoidance constraints. The path planning problem is translated into a decentralized partially observable Markov decision process (Dec-POMDP), which we solve through a deep reinforcement learning (DRL) approach, approximating the optimal UAV control policy without prior knowledge of the challenging wireless channel characteristics in dense urban environments. By exploiting a combination of centered global and local map representations of the environment that are fed into convolutional layers of the agents, we show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves, adapt to large complex environments and state spaces, and make movement decisions that balance data collection goals, flight-time efficiency, and navigation constraints. Finally, learning a control policy that generalizes over the scenario parameter space enables us to analyze the influence of individual parameters on collection performance and provide some intuition about system-level benefits.