In this paper, we investigate a computing task scheduling problem in
space-air-ground integrated network (SAGIN) for delay-oriented Internet of
Things (IoT) services. In the considered scenario, an unmanned aerial vehicle
(UAV) collects computing tasks from IoT devices and then makes online
offloading decisions, in which the tasks can be processed at the UAV or
offloaded to the nearby base station or the remote satellite. Our objective is
to design a task scheduling policy that minimizes offloading and computing
delay of all tasks given the UAV energy capacity constraint. To this end, we
first formulate the online scheduling problem as an energy-constrained Markov
decision process (MDP). Then, considering the task arrival dynamics, we develop
a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the
algorithm evaluates the risk, which measures the energy consumption that
exceeds the constraint, for each state and searches the optimal parameter
weighing the minimization of delay and risk while learning the optimal policy.
Extensive simulation results demonstrate that the proposed algorithm can reduce
the task processing delay by up to 30% compared to probabilistic configuration
methods while satisfying the UAV energy capacity constraint.

本文旨在设计一种任务调度策略，以最小化所有任务的离线和计算延迟，同时满足无人机能源容量约束下的延迟导向物联网服务需求，并考虑到任务到达动态变化的情况，提出了基于风险敏感的强化学习算法来解决能耗风险约束下的决策问题。