## 2 Related Works  
Several researches use traditional methods for intelligent routing control. Literature [10] introduces a node mobility prediction scheme to enhance the network throughput. The controller predicts node mobility through machine learning and sends optimal route information to the data plane in preparation for upcoming link failures. In the literature [11], a control mechanism based on context-driven is introduced in the scenario of SDN. It contributes to improving the autonomous capability of the network. In the literature [12], a hybrid network search path scheme is designed for intelligent traffic forwarding. Dijkstra and K path forwarding algorithm are used under different network loads. Literature [13] introduces a probabilistic-based QoS routing mechanism for SDN to reduce bandwidth blocking. Bayes' theorem and the Bayesian network model are used to determine the link probability and select the route. Artificial neural network extracts features data by imitating the way that neurons processes the input information. It is the main means of intelligent information processing nowadays. With the increase of GPU computing power, end-to-end models based on deep learning have become state-of-the-art in the fields of computer vision, natural language processing, and reinforcement learning [14]. The application of deep learning and reinforcement learning to network traffic control and routing forwarding is a relatively new application scenario which has received a lot of attention in recent years. [PERSON] et al. [15] introduce the deep convolutional neural network into the Wireless Mesh Network (WMN). The traffic pattern of sensor network is sent to the deep neural network in the form of multi-channel tensor for training, and the optimal routing strategy is obtained. In the literature [16], the author tries to replace the original routing strategy by training multiple restricted Boltzmann machines. Correspondingly, this can effectively reduce the data transmission delay and improve the overall transmission efficiency. However, this method is only suitable for small sensor networks. Once the number of nodes increases, the number of neural networks to be trained will also increase exponentially. In our previous work [17], we study the combination of deep learning and WSN with super nodes. Through link reliability prediction, the routing decision algorithm is introduced to reduce the overall transmission delay and effectively improve the network life. Literature [18] combines convolutional neural network with restricted Boltzmann machines to calculate routing for software-defined routers in wireless mesh sensor networks. Literature [19] uses the three-dimensional tensor formed by the time series traffic patterns of nodes in the network for training. It also explores the influence of the deep model and the shallow model on the training effect. Deep learning is also used in scenarios such as intelligent channel allocation and traffic prediction [18; 20]. Due to the characteristics of data transmission policy for software-defined sensor network, offline algorithms such as deep learning cannot match the dynamic characteristics of network data forwarding. In [21; 22], conversion of traditional routing rules into computational paradigms is investigated based on deep learning and reinforcement learning respectively. [PERSON] et al. [8] introduce reinforcement learning into the software-defined wireless sensor network. By designing a quadruple reward function and combining with the Q-Learning algorithm [23], it effectively improves the energy efficiency of the SDWSN and prolongs the lifetime of network nodes. Some routing planning algorithms are proposed based on Q-Learning, such as the QELAR model proposed by [PERSON] et al. [24], the SDWSN model proposed by [PERSON] et al. [25], and the DACR routing algorithm proposed by [PERSON] et al. [26]. These models are designed to improve the energy efficiency of nodes and the quality of service (QoS) of WSN.