Abstract: In order to solve the problems faced by radar system such as complex jamming scenes, low reliability and bad real-time performance, an intelligent decision generation model is constructed based on Deep Reinforcement Learning, where targeted action set, state set and reward function are designed. After that, a decision network training algorithm based on double deep Q-network is proposed to overcome the problem of Q value over estimation which caused by the coupling of target network and evaluation network in Deep Q-network (DQN). The simulation results show that, compared with DQN, Q learning and traversal algorithm, the intelligent decision model and training method designed in this paper have better interference suppression effect, stronger generalization ability and faster response time, and effectively improve the radar independent decision-making ability.