Before revision:  In this paper, an explainable deep reinforcement learning method is proposed to deal with the multirotor obstacle avoidance and navigation problem. Both visual and textual explanation is provided to make the trained agent more transparency and comprehensible for humans. Our model can provide real-time decision explanation for non-expert users. Also , some global explanation results are provided for experts to diagnose the learned policy.
After revision:  In this paper, an explainable deep reinforcement learning (DRL) method. Specifically, to get better understanding of the trained network, some model explanation methods are proposed. Based on the feature attribution, each decision making result during flight is explained using both visual and texture explanation. Moreover , some global explanation results are provided for experts to diagnose the learned policy.