Multi-UAV Interception Decision-Making via Meta Reinforcement Learning

Multi-UAV collaborative interception decision-making is a critical task in uncrewed aerial vehicle (UAV) applications. The core challenge lies in achieving real-time decision-making and precise coordination among multiple agents in highly dynamic and uncertain complex environments to successfully intercept highly maneuverable targets. However, existing methods still fall short in terms of rapid adaptability for interception decision-making, and the generalization performance of traditional reinforcement learning (RL) on new tasks needs improvement. To address these limitations, this article presents a novel meta-RL-based approach for multi-UAV interception decision-making. First, a task allocation method based on greedy strategy is proposed to achieve the rapid assignment of interception targets. Then, we design a task inference model that employs a probabilistic encoder to extract the shared structure across tasks from contextual information, thereby generating a latent variable to represent the task and enhance the training of the algorithm's network. Meanwhile, the proposed method is integrated with RL to improve its generalization capability in new tasks, enabling fast decision-making for the interception task. Finally, the comparative results against baseline algorithms validated the superiority of our method.