Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments

Chao Yan, Xiaojia Xiang, Chang Wang

Abstract: Path planning remains a challenge for Unmanned Aerial Vehicles (UAVs) in dynamic environments with potential threats. In this paper, we have proposed a Deep Reinforcement Learning (DRL) approach for UAV path planning based on the global situation information. We have chosen the STAGE Scenario software to provide the simulation environment where a situation assessment model is developed with consideration of the UAV survival probability under enemy radar detection and missile attack. We have employed the dueling double deep Q-networks (D3QN) algorithm that takes a set of situation maps as input to approximate the Q-values corresponding to all candidate actions. In addition, the ε-greedy strategy is combined with heuristic search rules to select an action. We have demonstrated the performance of the proposed method under both static and dynamic task settings.