Reinforcement Learning-based Methods for Cooperative Control of UAVs: Challenges and Perspectives

Unmanned Aerial Vehicles (UAVs), equipped with embodied technologies such as high-resolution cameras and AI-integrated sensors, have been widely deployed in diverse real-world applications. Reinforcement learning (RL) enables advanced real-time decision support for complex mission planning through interactions between the simulation environment and UAVs. Despite the transformative potential of machine learning (ML) frameworks for UAV swarms, critical challenges persist. Among these, ensuring model update synchronization to maintain spatiotemporal consistency between simulations and their physical counterparts remains the foremost challenge in collaborative UAV training environments. This paper systematically identifies and analyzes these key research challenges, laying a foundation for future advancements in this emerging field.