Cooperative Multi-UAV Target Exploration with Graph-Based Reinforcement Learning
Unmanned aerial vehicles (UAVs) offer several advantages, including high mobility, flexible deployment, low cost, and strong adaptability to complex environments, making them highly promising for applications such as disaster search and rescue, environmental monitoring, inspection, and reconnaissance. For target exploration tasks in unknown environments, multiUAV systems can expand the search area, improve exploration efficiency, and enhance the robustness of task execution through cooperation, which makes this problem of significant research interest. However, such tasks still face several challenges, including partial observability of environmental information, complex cooperative decision-making, and difficulties in credit assignment among multiple UAVs. Reinforcement learning is capable of learning decision-making policies autonomously through interaction with the environment, providing a new perspective for solving cooperative exploration problems in complex environments. To address these issues, we propose a cooperative decision-making method for multi-UAV target exploration. By incorporating target-related information, the proposed method enhances the cooperative exploration capability of UAVs in unknown environments, while a tailored reward design is adopted to improve the coordination efficiency of multiple UAVs. Experimental results show that the proposed method exhibits strong adaptability to different team sizes and sensor configurations, learns effective cooperative behaviors, and outperforms classical exploration methods across multiple performance metrics, thereby demonstrating its effectiveness in multi-UAV target exploration tasks.