Reinforcement Learning-Based Target Assignment Method for Many-to-Many Interceptions
GUO Jianguo1， HU Guanjie1， XU Xinpeng1，2， LIU Yue2， CAO Jin2
1. Institute of Precision Guidance and Control， School of Astronautics， Northwestern Polytechnical University， Xi’an 710072， Shaanxi， China；
2. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China
Abstract：Aiming at the issue of weapon target assignment for a many-to-many interception in the air confrontation environment, this study has proposed a multi-target intelligent assignment method based on reinforcement learning. Under the many-to-many interception engagement scenario, a mathematical model of target assignment was established based on the engagement posture evaluation. By introducing the concepts of target threat degree and interception effectiveness degree, the interception urgency of each target and the interception capability characterization of each interceptor were fully reflected, allowing a comprehensive evaluation of the engagement posture of the attacking and defending sides. Based on the target assignment model, the target assignment issue was built up using a Markov decision process and was trained to be solved by a reinforcement learning algorithm using deep Q-network. Relying on the self-learning and reward mechanism under environment interaction, the dynamic generation of optimal assignment schemes was effectively realized. A many-to-many interception scenario was created and its effectiveness was verified through mathematical simulation, and the result shows that the trained target assignment method satisfies the requirements of continuous and dynamic task assignment in many-to-many interception.
