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:在多个追击者的敌对攻击下,机载设备的安全导航是一项具有挑战性的任务。本文提出了一种新颖的方法——异步多阶段深度强化学习(AMS-DRL),通过训练一个可以从多个追击者的行为中学习并快速适应其行为的对抗神经网络,使得机载设备能够避开攻击并达到其目标。我们的方法通过保证博弈理论中的纳什均衡来保证收敛性。我们在广泛的模拟中评估了我们的方法,并表明它优于具有更高导航成功率的基线。我们还分析了相对最大速度等参数如何影响导航性能。此外,我们进行了物理实验,并验证了训练策略在实时飞行中的有效性。介绍了一个成功率热图,以阐明空间几何如何影响导航结果。项目网站: https://github.com/NTU-UAVG/AMS-DRL-for-Pursuit-Evasion。