基于MADDPG的多无人机协同任务决策

Aiming at the problem that the traditional optimization algorithm is difficult to get the desired results in a short time in the research of multi-UAV (unmanned aerial vehicle) task decision-making method, this paper proposes a multi-agent deep deterministic policy gradient (MADDPG) algorithm based on deep reinforcement learning. It allows UAVs to use global information in learning and only local information in application decision-making. The model structure of MADDPG algorithm is designed. Finally, through simulation experiments and comparing with deep deterministic policy gradient (DDPG) algorithm, it is verified that the MADDPG algorithm proposed in this paper can greatly improve the learning speed on the basis of ensuring the accuracy, and make up for the shortcomings of the traditional reinforcement learning algorithm in the field of multiple agents.