Intelligent maneuver decision-making for UAVs using the TD3-LSTM reinforcement learning algorithm under uncertain information

Aiming to address the complexity and uncertainty of unmanned aerial vehicle (UAV) aerial confrontation, a twin delayed deep deterministic policy gradient (TD3)-long short-term memory (LSTM) reinforcement learning-based intelligent maneuver decision-making method is developed in this paper. A victory/defeat adjudication model is established, considering the operational capability of UAVs based on an aerial confrontation scenario and the 3-degree-of-freedom (3-DOF) UAV model. For the purpose of assisting UAVs in making maneuvering decisions in continuous action space, a model-driven state transition update mechanism is designed. The uncertainty is represented using the Wasserstein distance and memory nominal distribution methods to estimate the detection noise of the target. On the basis of TD3, an LSTM network is utilized to extract features from high-dimensional aerial confrontation situations with uncertainty. The effectiveness of the proposed method is verified by conducting four different aerial confrontation simulation experiments.