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A deep reinforcement learning technique is presented for task offloadingdecision-making algorithms for a multi-access edge computing (MEC) assistedunmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT)environment. The task offloading technique uses financial concepts such as costfunctions and conditional variable at risk (CVaR) in order to quantify thedamage that may be caused by each risky action. The approach was able toquantify potential risks to train the reinforcement learning agent to avoidrisky behaviors that will lead to irreversible consequences for the farm. Suchconsequences include an undetected fire, pest infestation, or a UAV beingunusable. The proposed CVaR-based technique was compared to other deepreinforcement learning techniques and two fixed rule-based techniques. Thesimulation results show that the CVaR-based risk quantifying method eliminatedthe most dangerous risk, which was exceeding the deadline for a fire detectiontask. As a result, it reduced the total number of deadline violations with anegligible increase in energy consumption.

To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs