A Feature-Aware Elite Imitation MARL for Multi-UAV Trajectory Optimization in Mountain Terrain Detection

With the advancement of UAV trajectory planning and sensing technologies, unmanned aerial vehicles (UAVs) are now capable of performing high-performance ground detection and search tasks. Mountainous regions, due to their complex terrain, have long been a focal point in the field of remote sensing. Effective UAV search tasks in such areas must consider not only horizontal coverage but also variations in detection range and angle caused by changes in elevation. Conventional algorithms typically require complete prior knowledge of the environment for trajectory optimization and often depend on scenario-specific policy models, limiting their generalizability. To address these challenges, this paper proposes a Feature-Aware Elite Imitation Multi-Agent Reinforcement Learning (FA-EIMARL) algorithm that leverages partial terrain information to construct a feature extraction network. This approach enables batch training across diverse terrains without the need for full environmental maps. In addition, an elite imitation mechanism has been proposed for convergence acceleration and task performance enhancement. Simulation results demonstrate that the proposed method achieves superior reward performance, convergence rate, and computational efficiency while maintaining strong adaptability to varying terrains.