Unmanned Aerial Vehicles (UAVs) increasingly enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic, obstacle-prone environments remains a significant research challenge due to their complexity. Reinforcement Learning (RL) offers adaptability and robustness in such environments, proving effective for UAV optimization. This paper introduces RLpos-3, a novel framework that integrates standard RL techniques and simulation libraries with Network Simulator 3 (ns-3) to facilitate the development and evaluation of UAV positioning algorithms. RLpos-3 serves as a supplementary tool for researchers, enabling the implementation, analysis, and benchmarking of UAV positioning strategies across diverse environmental conditions while meeting user traffic demands. To validate its effectiveness, we present use cases demonstrating RLpos-3's performance in optimizing UAV placement under realistic conditions, such as urban and obstacle-rich environments.
@article{arxiv.2502.08787,
title = {A Framework to Develop and Validate RL-Based Obstacle-Aware UAV Positioning Algorithms},
author = {Kamran Shafafi and Manuel Ricardo and Rui Campos},
journal= {arXiv preprint arXiv:2502.08787},
year = {2026}
}