## 2 Related Works  
The traffic scheduling of a DCN is used to solve the problem of when and at what rate each data flow in the network should be transmitted. DCNs require efficient, flexible, and secure traffic scheduling methods to optimize routing for different types of traffic. Currently, there are numerous research efforts focused on traffic scheduling in DCNs. Traditional traffic scheduling methods include the Shortest Path First (SPF) algorithm, the Equal-cost Multi-path (ECMP) [8] algorithm, and the heuristic algorithms. The SPF algorithm is simple, but it has the drawback of being unable to fully utilize network resources. As a traditional static traffic scheduling method, ECMP lacks congestion awareness and may map multiple large flows onto the same path, resulting in congestion in the network. The current dynamic traffic scheduling algorithms, such as FDALB [9] and DIFFERENTEC [10], primarily focus on large flows. The dynamic traffic scheduling based on SDNs on links with congestion or load imbalance can effectively reduce the probability of link congestion and improve the transmission performance of the traffic. Among them,scheduling schemes such as Hedera [11], Mahout [12], Nimble [13], and Fincher are often used to distinguish between large and small streams. Heuristic algorithms [14] are proposed relative to optimization algorithms, which are generally used to solve non-deterministic polynomial problems, providing approximate optimal solutions. The swarm intelligence algorithm is a heuristic algorithm used to find the global optimal solution. In recent years, it has been commonly used in data center network traffic scheduling technology to find the optimal scheduling route for network traffic efficiently and quickly. For example, Ant Colony Optimization [15] and Particle Swarm Optimization, which belong to swarm intelligence algorithms, have been combined with traffic scheduling problems. However, these algorithms also have certain problems and shortcomings, such as complex computation, a slow convergence speed in the later stage, an inability to simultaneously achieve the calculation speed and result accuracy, a susceptibility to local optima, and an inability to guarantee the global optimality of solutions. They are unable to cope with rapid network traffic changes and therefore cannot perfectly meet the requirements of network performance. In recent years, with the rapid development of machine learning technology, researchers have introduced and applied it to SDNs based on the effectiveness of machine learning technology in policy decision-making for routing scheduling issues in complex networks and conducted many related studies. [PERSON] [16] provided a comprehensive overview of the research on utilizing reinforcement learning (RL) methods to solve routing problems, summarizing commonly used reinforcement learning models and the current challenges. [PERSON] et al. [17; 18; 19; 20] proposed a Q-Learning based routing algorithm for sensor networks and opportunistic networks. When learning convergence is difficult due to the increase in the state-space dimension, using neural networks to estimate the Q-value can solve the problem of dimension explosion while reducing the average latency and improving network throughput. Reference [21] introduced RL neural networks into the QoS routing calculation; it proposed a variable greedy function and improved the reward function to optimize the routing decision model. This routing scheme consumes excessive controller resources and has long computation times, resulting in poor timeliness. [PERSON] et al. [22] transformed the path planning problem into a sequence inference problem of network nodes, using a sequence-to-sequence model to learn forwarding paths based on network traffic. These approaches are centered on a single node and do not consider traffic scheduling problems from the perspective of the overall network.