Computation Offloading Optimization in Satellite-Terrestrial Integrated Networks via Offline Deep Reinforcement Learning

As the demand for global internet connectivity continues to grow, the satellite-terrestrial integrated networks (STINs) have become more and more crucial for expanding the service coverage and enhancing the network performance. However, the task offloading problem in STINs faces many significant challenges such as high processing latency and energy consumption. The current intelligent offloading strategies often rely on the real-time interactions with the environments which not only consume valuable satellite resources but also cause irreversible damage to the satellite equipment due to some operational errors. To address these issues, in this paper, we propose an offline deep reinforcement learning (offline DRL) approach to learn and optimize the task offloading decisions by leveraging the stored historical decision data and employing the soft actor-critic (SAC) algorithm specifically. Experimental results show that the proposed strategy outperforms most of the existing methods in terms of latency and energy consumption and effectively reduces the direct interactions with STINs.