Explainable Deep Reinforcement Learning for UAV autonomous path planning

Autonomous navigation in unknown environment is still a hard problem for small Unmanned Aerial Vehicles (UAVs). Recently, some neural network-based methods are proposed to tackle this problem, however, the trained network is opaque, non-intuitive and difficult for people to understand, which limits the real-world application. In this paper, a novel explainable deep neural network-based path planner is proposed for quadrotor to fly autonomously in unknown environment. The navigation problem is modelled as a Markov Decision Process (MDP) and the path planner is trained using Deep Reinforcement Learning (DRL) method in simulation environment. To get better understanding of the trained model, a novel model explanation method is proposed based on the feature attribution. Some easy-to-interpret textual and visual explanations are generated to allow end-users to understand what triggered a particular behaviour. Moreover, some global analyses are provided for experts to evaluate and improve the trained network. Finally, real-world flight tests are conducted to illustrate that our path planner trained in the simulation is robust enough to be applied in the real environment directly.