Cooperative Multi-Type Multi-Agent Deep Reinforcement Learning for
  Resource Management in Space-Air-Ground Integrated Networks
Hengxi Zhang Huaze Tang Wenbo Ding Xiao-Ping Zhang
The Space-Air-Ground Integrated Network (SAGIN), integrating heterogeneous
devices including low earth orbit (LEO) satellites, unmanned aerial vehicles
(UAVs), and ground users (GUs), holds significant promise for advancing smart
city applications. However, resource management of the SAGIN is a challenge
requiring urgent study in that inappropriate resource management will cause
poor data transmission, and hence affect the services in smart cities. In this
paper, we develop a comprehensive SAGIN system that encompasses five distinct
communication links and propose an efficient cooperative multi-type multi-agent
deep reinforcement learning (CMT-MARL) method to address the resource
management issue. The experimental results highlight the efficacy of the
proposed CMT-MARL, as evidenced by key performance indicators such as the
overall transmission rate and transmission success rate. These results
underscore the potential value and feasibility of future implementation of the
SAGIN.