  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 adversarial neural networks that can learn from the actions of multiple
evolved pursuers and adapt quickly to their behavior, enabling the drone to
avoid attacks and reach its target. Specifically, AMS-DRL evolves adversarial
agents in a pursuit-evasion game where the pursuers and the evader are
asynchronously trained in a bipartite graph way during multiple stages. 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-ICG/AMS-DRL-for-Pursuit-Evasion.
