Safe navigation of drones in the presence of adversarial physical attacks from multiple pursuers is a challenging task. This paper proposes a novel approach, asynchronous multi-stage deep reinforcement learning (AMS-DRL), to train an adversarial neural network that can learn from the actions of multiple pursuers and adapt quickly to their behavior, enabling the drone to avoid attacks and reach its target. Our approach guarantees convergence by ensuring Nash Equilibrium among agents from the game-theory analysis. We evaluate our method in extensive simulations and show that it outperforms baselines with higher navigation success rates. We also analyze how parameters such as the relative maximum speed affect navigation performance. Furthermore, we have conducted physical experiments and validated the effectiveness of the trained policies in real-time flights. A success rate heatmap is introduced to elucidate how spatial geometry influences navigation outcomes. Project website: https://github.com/NTU-UAVG/AMS-DRL-for-Pursuit-Evasion.

提出一种异步多阶段深度强化学习的方法（AMS-DRL）来训练对抗神经网络，以应对多个追赶者的攻击并快速适应其行为，确保无人机避免攻击并达到目标。该方法通过保证博弈论分析中的纳什均衡保证收敛性，并在大量模拟中进行了评估，展示其胜过基线的导航成功率。同时，实施了实物实验以验证训练出的策略在实时飞行中的有效性。

AMS-DRL: 机载多目标逃逸安全导航的学习