  In remote regions (e.g., mountain and desert), cellular networks are usually
sparsely deployed or unavailable. With the appearance of new applications
(e.g., industrial automation and environment monitoring) in remote regions,
resource-constrained terminals become unable to meet the latency requirements.
Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link
will lead to high delay. To tackle above issues, Satellite Edge Computing
architecture is proposed, i.e., users can offload computing tasks to visible
satellites for executing. However, existing works are usually limited to
offload tasks in pure satellite networks, and make offloading decisions based
on the predefined models of users. Besides, the runtime consumption of existing
algorithms is rather high.
  In this paper, we study the task offloading problem in satellite-terrestrial
edge computing networks, where tasks can be executed by satellite or urban TC.
The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm
can accelerate learning process by adjusting the number of candidate locations.
In addition, offloading location and bandwidth allocation only depend on the
current channel states. Simulation results show that DRTO achieves near-optimal
offloading cost performance with much less runtime consumption, which is more
suitable for satellite-terrestrial network with fast fading channel.
