  With the advancement of artificial intelligence technology, the automation of
network management, also known as Autonomous Driving Networks (ADN), is gaining
widespread attention. The network management has shifted from traditional
homogeneity and centralization to heterogeneity and decentralization.
Multi-agent deep reinforcement learning (MADRL) allows agents to make decisions
based on local observations independently. This approach is in line with the
needs of automation and has garnered significant attention from academia and
industry. In a distributed environment, information interaction between agents
can effectively address the non-stationarity problem of multiple agents and
promote cooperation. Therefore, in this survey, we first examined the
application of MADRL in network management, including specific application
fields such as traffic engineering, wireless network access, power control, and
network security. Then, we conducted a detailed analysis of communication
behavior between agents, including communication schemes, communication content
construction, communication object selection, message processing, and
communication constraints. Finally, we discussed the open issues and future
research directions of agent communication in MADRL for future network
management and ADN applications.
