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This paper analyzes the challenges of applying reinforcement learning (RL) to cooperative control of UAVs, focusing on the critical issue of maintaining spatiotemporal consistency between simulation environments and physical UAVs during model updates. It highlights the difficulties in ensuring synchronized model updates across UAVs to maintain consistent behavior. The paper serves as a foundational analysis of the key obstacles hindering the advancement of RL-based UAV swarm control.
Coordinating UAV swarms with RL faces a major hurdle: keeping simulated training aligned with the real world, especially during model updates.
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.
