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.

翻译：本文提出了一种深度强化学习技术，用于智慧农场物联网环境下多接入边缘计算辅助无人机网络的任务卸载决策算法。该任务卸载技术运用成本函数和条件风险价值等金融概念，量化每个风险行为可能造成的损害。该方法能够量化潜在风险，训练强化学习代理避免导致农场不可逆后果的风险行为，例如未检测到的火灾、虫害或无人机无法使用。将所提出的基于条件风险价值的技术与其他深度强化学习技术及两种固定规则技术进行了比较。仿真结果表明，基于条件风险价值的风险量化方法消除了最危险的风险——即超过火灾检测任务的截止时间。因此，它在能量消耗可忽略不计的增加下，减少了违反截止时间的总数。