基于CGAN的避扰通信决策网络离线式训练方法

基于强化学习的避扰通信，由于需要不断地与环境交互从中学习到最优决策，其决策网络的训练时间受环境反馈速率的约束，通常耗时严重。针对这一问题，提出了一种离线式训练方法。构建出一种频谱虚拟环境生成器，可以快速生成大量的逼真合成频谱瀑布图，用于避扰通信决策网络训练。由于所提方法脱离真实环境反馈，形成离线式训练，进而显著提高模型训练效率。实验结果表明:与实时在线训练方法比较，所提离线式训练方法的训练时间可以减少50%以上。 

An offline training method using CGAN for anti-jamming communication decision network

Due to the continuous interaction with the environment to learn the optimal decision, the training time of the decision network based on reinforcement learning is restricted by the feedback rate of the environment, which usually consumes a lot of time. To solve this problem, an offline training method is proposed. A spectrum virtual environment generator is constructed, which can quickly generate a large number of realistic synthetic spectrum waterfall images for the training of anti-jamming communication decision network. Because the method is separated from the real environment feedback, the offline training is formed and the efficiency of model training is improved significantly. Experimental results show that the training time of this offline method is reduced by more than 50% compared with the online real-time training method.