  Traditional ground wireless communication networks cannot provide
high-quality services for artificial intelligence (AI) applications such as
intelligent transportation systems (ITS) due to deployment, coverage and
capacity issues. The space-air-ground integrated network (SAGIN) has become a
research focus in the industry. Compared with traditional wireless
communication networks, SAGIN is more flexible and reliable, and it has wider
coverage and higher quality of seamless connection. However, due to its
inherent heterogeneity, time-varying and self-organizing characteristics, the
deployment and use of SAGIN still faces huge challenges, among which the
orchestration of heterogeneous resources is a key issue. Based on virtual
network architecture and deep reinforcement learning (DRL), we model SAGIN's
heterogeneous resource orchestration as a multi-domain virtual network
embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We
model the different network segments of SAGIN, and set the network attributes
according to the actual situation of SAGIN and user needs. In DRL, the agent is
acted by a five-layer policy network. We build a feature matrix based on
network attributes extracted from SAGIN and use it as the agent training
environment. Through training, the probability of each underlying node being
embedded can be derived. In test phase, we complete the embedding process of
virtual nodes and links in turn based on this probability. Finally, we verify
the effectiveness of the algorithm from both training and testing.
