Air defense intelligent weapon target assignment method based on deep reinforcement learning

Aiming at the problems of the existing target allocation methods in practical application, such as insufficient representation of game antagonism and weak representation of tacit knowledge in the combat process, this paper studies the intelligent target assignment method based on deep reinforcement learning. Firstly, based on the operational characteristics of air defense target assignment, a new type of deep neural network for high-dimensional "state action" space is established, and the input and output information categories of the network, the state space and action space of each node are studied. The reward function is designed, and the strategy parameters are smoothly optimized by asynchronous training in the digital battlefield simulation environment by using the near end strategy optimization algorithm with tailoring. Simulation results show that the intelligent target assignment neural network model proposed in this paper has advantages and applicability.