# DRL-Based Load-Balancing Routing Scheme for 6G Space-Air-Ground Integrated Networks
## 1 Introduction  
In the last few years, the space-air-ground integrated network has developed rapidly. Satellite communication is a key link in 6G space-air-ground integrated networks (SAGIN). It can make up for the shortcomings of the 5G terrestrial network, improve network coverage, and ensure the fault tolerance of the system. It can also combine artificial intelligence, big data, the Internet of Things, and other technologies to provide users with diverse services. Satellite communication has a larger coverage area than traditional terrestrial networks, and its global adaptability is stronger. It is gradually becoming the main competitive technology for the next-generation communications. The importance of Low Earth Orbit (LEO) in the field of satellite communications cannot be overstated. When compared to the constellations of Geosynchronous Orbit and Medium Earth Orbit, LEO stands out due to its low transmission delay, low propagation loss, and its ability to cover the world. These unique features make LEO an attractive option for various applications, including internet services, global positioning systems, and remote sensing. The low transmission delay and low propagation loss of LEO makes it an ideal choice for time-sensitive applications such as real-time communication, while its global coverage ensures that it is suitable for applications that require connectivity in remote or hard-to-reach locations. Therefore, it is not surprising that LEO has gained significant attention and interest in recent years, leadingto the development of new technologies and algorithms to enhance its performance and efficiency. With the rapid development of SAGIN, the traditional ground communication network can not adapt to future development. The development of satellite communication in low earth orbit is already a promising development direction. In LEO satellite networks, inter-satellite links (ISLs) ensure communication between satellites. Compared with the terrestrial communication network, the LEO satellite network changes its topology more frequently, has a longer inter-satellite link delay, and has a link state in the multiuser area that changes more frequently. Due to the high-speed dynamic changes, the cost of traditional path selection methods is significantly increased. Therefore, the routing protocol for terrestrial applications is difficult to use directly in the LEO satellite network. LEO satellite routing technology is also a supporting technology for the integration of remote sensing, communication, and computing for 6G SAGIN; so, it is necessary to study the routing algorithm in the low-orbit satellite network. Most of the existing satellite routing algorithms have been developed based on terrestrial-network routing algorithms. Most of these algorithms are based on the shortest path. Due to the difference in the density of satellites at high and low latitudes and the difference in the density of user distribution [1], the load differences among satellites in the same constellation are large. In addition, with the high-speed movement of the satellites, the high-load coverage area also changes rapidly among satellites. Therefore, the traditional routing algorithm has faced difficulties in meeting the current development of satellite networks. The cognitive performance of the deep learning algorithm is better, the decision-making ability of the reinforcement learning algorithm is stronger, and deep reinforcement learning combines the two. In deep reinforcement learning, an agent makes decisions through interaction with the environment and obtains feedback through trial and error, learning to maximize the reward and minimize the punishment [2]. Due to the powerful perception and decision-making ability of deep RL, more and more scholars have applied this type of learning in many fields such as computer vision [3,4], speech recognition, and automatic driving [4,5]. Deep reinforcement learning is also applicable in the field of LEO satellite networks.