In this paper, we introduce an Imagine Network that can simulate itself through graph tree neural networks. Among the graph tree neural networks models, association, deduction, and memory networks are learned, and a network is created by combining the discriminator and reinforcement learning models. This model can learn various datasets or data samples generated in environments and generate new data samples.

翻译：在本文中,我们引入了一个可以通过图形树神经网络模拟的想象网络。 在图形树神经网络模型中,我们学习了关联、扣减和记忆网络,并且通过将歧视与强化学习模型相结合来创建网络。 这一模型可以学习环境中产生的各种数据集或数据样本,并产生新的数据样本。