A deep reinforcement learning technique is presented for task offloading
decision-making algorithms for a multi-access edge computing (MEC) assisted
unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT)
environment. The task offloading technique uses financial concepts such as cost
functions and conditional variable at risk (CVaR) in order to quantify the
damage that may be caused by each risky action. The approach was able to
quantify potential risks to train the reinforcement learning agent to avoid
risky behaviors that will lead to irreversible consequences for the farm. Such
consequences include an undetected fire, pest infestation, or a UAV being
unusable. The proposed CVaR-based technique was compared to other deep
reinforcement learning techniques and two fixed rule-based techniques. The
simulation results show that the CVaR-based risk quantifying method eliminated
the most dangerous risk, which was exceeding the deadline for a fire detection
task. As a result, it reduced the total number of deadline violations with a
negligible increase in energy consumption.

本文介绍了一种基于 CVaR 的多边形无人机任务卸载决策方法，它利用深度强化学习技术，以及开发一个智能农场物联网环境中 MEC 协同的无人机网络。结果表明，所提出的 CVaR 方法成功地将风险量化，并能训练强化学习智能体避免可能引发农场不可逆后果的高风险行为，这是传统方法所不具备的。