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.

翻译：在存在多追击者物理对抗攻击的情况下，实现无人机安全导航是一项极具挑战性的任务。本文提出一种新颖的异步多阶段深度强化学习（AMS-DRL）方法，通过训练对抗性神经网络从多追击者的行动中学习并快速适应其行为模式，使无人机能够规避攻击并抵达目标。基于博弈论分析，我们通过确保智能体间达成纳什均衡来保证算法的收敛性。在大量仿真实验中，该方法以更高导航成功率优于基线方法，并分析了相对最大速度等参数对导航性能的影响。此外，通过实物飞行实验验证了训练策略在实时场景中的有效性。我们引入成功率热力图来揭示空间几何结构如何影响导航结果。项目网站：https://github.com/NTU-UAVG/AMS-DRL-for-Pursuit-Evasion