Attention-Based Augmented Reinforcement Learning Algorithm for Multi-UAV Navigation

Multi-UAV autonomous navigation serves as a fundamental basis for a wide array of applications across various fields. However, achieving cooperative navigation in complex and unknown three-dimensional environments remains a significant challenge. In this paper, a novel attention-based augmented reinforcement learning algorithm is proposed for multi-UAV navigation (A2RL-NAV) in 3D environments. Specifically, we incorporate an LSTM network along with an attention mechanism within the actor network. Additionally, we design some goal-oriented reward functions to address the issue of sparse rewards. Finally, we validate the effectiveness and robustness of our approach by comparing the proposed algorithm with several baseline algorithms through simulation experiments.