Multi-UAV roundup strategy method based on deep reinforcement learning CEL-MADDPG algorithm

Abstract

Due to the complexity of the multi-UAV rounding up maneuvering target task in continuous and complex environments, it is difficult for the UAVs to quickly and accurately capture maneuvering targets. Therefore, this paper proposes CEL-MADDPG algorithm based on Curriculum Experience Learning. It improves the efficiency of multi-UAVs rounding up maneuvering target, and has certain generalization. which is better applied to the multi-UAV roundup task in complex dynamic environments. The main contributions are the following two: By introducing the Curriculum Experience Learning, the multi-UAV rounding up task is divided into target tracking, encircling transition, and shrinking capture to learn, and designed corresponding reward function according to the task characteristics of each subtask. Which improves the learning efficiency of the model. Additionally, the CEL-MADDPG adopts the Preferential Experience Replay strategy to select experiences that are conducive to accelerating network convergence, and the experience most similar to the current state is further selected as a learning sample by using Relative Experience Learning (REL). This improves the sampling efficiency of samples and the training and optimization efficiency of the model. Simulation experiments show that the CEL-MADDPG algorithm can effectively improve the training efficiency of the model and has higher task completion efficiency.