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Space-air-ground integrated network (SAGIN) is a new type of wireless networkmode. The effective management of SAGIN resources is a prerequisite forhigh-reliability communication. However, the storage capacity of space-airnetwork segment is extremely limited. The air servers also do not havesufficient storage resources to centrally accommodate the information uploadedby each edge server. So the problem of how to coordinate the storage resourcesof SAGIN has arisen. This paper proposes a SAGIN storage resource managementalgorithm based on distributed deep reinforcement learning (DRL). The resourcemanagement process is modeled as a Markov decision model. In each edge physicaldomain, we extract the network attributes represented by storage resources forthe agent to build a training environment, so as to realize the distributedtraining. In addition, we propose a SAGIN resource management framework basedon distributed DRL. Simulation results show that the agent has an idealtraining effect. Compared with other algorithms, the resource allocationrevenue and user request acceptance rate of the proposed algorithm areincreased by about 18.15\% and 8.35\% respectively. Besides, the proposedalgorithm has good flexibility in dealing with the changes of resourceconditions.

Incorporating Distributed DRL into Storage Resource Optimization of Space-Air-Ground Integrated Wireless Communication Network