A reinforcement learning-based swarm intelligent decision-making method of cooperative multi-target attack under high-dynamic situation is proposed. The composite evaluation criteria of attack performance is established

including the evaluation of attack superiority based on relative motion information and the threat evaluation based on the inherent information of target. To evaluate the attack-defence effectiveness

a cost-effectiveness ratio index is designed by combining attack performance

penetration probability and attack cost together. In addition

a multi-target decision-making architecture based on reinforcement learning is constructed

and an action space with allocation vectors as basic elements and a state space based on quantified performance indicators are designed. Q-Learning is employed to make intelligent decisions on cooperative attack plans

including missile selection and target assignment. The simulated results show that reinforcement learning can achieve multi-target online decision-making with the optimal offensive and defensive effectiveness

and its computational efficiency has more obvious advantages than that of particle swarm optimizer.