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In recent years, the amalgamation of satellite communications and aerialplatforms into space-air-ground integrated network (SAGINs) has emerged as anindispensable area of research for future communications due to the globalcoverage capacity of low Earth orbit (LEO) satellites and the flexibleDeployment of aerial platforms. This paper presents a deep reinforcementlearning (DRL)-based approach for the joint optimization of offloading andresource allocation in hybrid cloud and multi-access edge computing (MEC)scenarios within SAGINs. The proposed system considers the presence of multiplesatellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks fromground users are modeled as directed acyclic graphs (DAGs). With the goal ofreducing energy consumption and latency in MEC, we propose a novel multi-agentalgorithm based on DRL that optimizes both the offloading strategy and theallocation of resources in the MEC infrastructure within SAGIN. A hybrid actionalgorithm is utilized to address the challenge of hybrid continuous anddiscrete action space in the proposed problems, and a decision-assisted DRLmethod is adopted to reduce the impact of unavailable actions in the trainingprocess of DRL. Through extensive simulations, the results demonstrate theefficacy of the proposed learning-based scheme, the proposed approachconsistently outperforms benchmark schemes, highlighting its superiorperformance and potential for practical applications.

Joint Offloading and Resource Allocation for Hybrid Cloud and Edge  Computing in SAGINs: A Decision Assisted Hybrid Action Space Deep  Reinforcement Learning Approach