Multi-UAV Reinforcement Learning With Realistic Communication Models: Recent Advances and Challenges

The interest in applications related to Multi-Unmanned Aerial Vehicle (UAV) systems has been growing exponentially inthe last few years. Reinforcement Learning (RL) presents one of the most popular alternatives for solving Multi-UAV tasks, thanks to its flexible requirements for modelingthe problem. However, it is often applied to abstractions of the original problem, thus leaving to next development phases the integration of RL solutions to the actual systems. This choice may not guarantee the overall optimal performance of the implemented system. In this survey, we analyze the literature on Multi-UAV applications that utilize reinforcement learning, with particular attention to works that consider realistic communication channels. We focus on identifying the key variables that influence communication and whether these variables are integrated within the RL framework or considered externally. Additionally, we identify key trends, challenges, and future directions in the field, providing a comprehensive overview for researchers and practitioners interested in the practical deployment of RL-based Multi-UAV systems.