Reward shaping based reinforcement learning for intelligent missile penetration attack strategy planning - LUO Junren; LIU Guo; SU Jiongming; ZHANG Wanpeng; CHEN Jing (智能科学与技术学报)
Facing the future requirements of distributed warfare at sea, the strategic planning of missile penetration is firstly analyzed based on the background of intelligent missile salvo penetration against surface ships in distributed warfare scenario. Secondly, a strategic planning method of intelligent missile penetration based on reward-shaping reinforcement learning is designed by using multi-class reward function. Then, the operation scenario of the missile penetration ship is constructed on the Mozi joint operation simulation system. The comparison experiment shows that the success rate of the intelligent missile penetration attack controlled by the model learned by the reward molding method is 79%, which verifies the effectiveness of the reward-based reinforcement learning method. Finally, after action review, it is found that there are emerging four kinds of penetration strategies of intelligent missiles in the reward shaping experiment, including concentrated and roundabout attack, scattered penetration multi-direction attack, group delay attack and cruise detection guide attack.