  As adversarial attacks pose a serious threat to the security of AI system in
practice, such attacks have been extensively studied in the context of computer
vision applications. However, few attentions have been paid to the adversarial
research on automatic path finding. In this paper, we show dominant adversarial
examples are effective when targeting A3C path finding, and design a Common
Dominant Adversarial Examples Generation Method (CDG) to generate dominant
adversarial examples against any given map. In addition, we propose Gradient
Band-based Adversarial Training, which trained with a single randomly choose
dominant adversarial example without taking any modification, to realize the
"1:N" attack immunity for generalized dominant adversarial examples. Extensive
experimental results show that, the lowest generation precision for CDG
algorithm is 91.91%, and the lowest immune precision for Gradient Band-based
Adversarial Training is 93.89%, which can prove that our method can realize the
generalized attack immunity of A3C path finding with a high confidence.
