  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.
