| Citation: | JIANG Minmin, LI Dapeng, QIU Xin, et al. An offline training method using CGAN for anti-jamming communication decision network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1412-1421. doi: 10.13700/j.bh.1001-5965.2019.0448(in Chinese)
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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.
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