## 3 Related Studies and Motivation
### Supervised Learning-Based Network Routing  
Supervised learning is a method of learning the correct answer data to predict values and categories for new data [21]. In [22], the authors proposed graph-aware deep-learning-based intelligent routing to solve such problems as slow fusion and degradation that occur when constructing complex dynamic networking conditions. In addition, in [23], the authors proposed an ML-assisted left-loaded algorithm, which is a combination of the Bayes classifier and the existing least-loaded algorithm, configured to solve the connection failure problem in the following situations of a circuit-switched network. Moreover, the authors of [24; 25] proposed a RouteNet algorithm to help optimize routing schemes for mean delays by leveraging the ability of graph neural networks (GNNs) to learn and model graph-based information. In [26], the authors proposed a deep learning routing algorithm that configures a deep-learning-based neural network, with the input of the model consisting of a set of numbers indicating the number of packets forwarded through each node of the network, and the output using network traffic information in the form of an interface for forwarding packets. In addition, in [27], the authors studied the input of the deep learning model as a routing strategy based on the deep learning architecture. The deep believe architecture (DBA) was defined between the number of inbound packets observed on each router and the time interval, and an algorithm was proposed to construct a restricted Boltzmann machine on the detailed hidden layer of the DBA. In [28], the authors also proposed an automated protocol design method for controlling and managing a network by applying two important aspects of the GNN model--i.e., distributing the topology information between different nodes and calculating the topology and link-weighted paths. In addition, in [29], the authors also proposed MLProp by leveraging a variety of input parameters--such as the buffer capacity and node access rates, respectively--to indicate whether packets can be delivered successfully along these links, improving the network link performance and probabilistic routing approaches to intermittent opportunistic networking. Finally, in [30], the authors developed a flexible supervised learning framework through a training model that includes learning paths and all path pairs of mixed integral linear programming and proposed a routing methodology that utilizes deep neural network methods to minimize the identity of network systems instead of always selecting the shortest paths.  
Related studies on supervised-learning-based network routing methodologies predict the routing paths by learning the machine learning models, such as naive Bayes classification, decision tree, and deep learning-based neural network models. However, to conduct supervised learning, a dataset must be prepared in advance, and the performance varies greatly depending on the type of model used. In addition, although the correct labels for the datasets used must exist, it is difficult to produce an efficient predictive path because the correct labels can always vary depending on the bandwidth and traffic intensity generated by the actual network. Therefore, DQN-EC can yield efficient routing paths compared to studies related to supervised learning-based networks because the approach collects both static and dynamic data through SDN controllers and utilizes deep reinforcement learning, which does not select specific models.