  This paper studies the trajectory control and task offloading (TCTO) problem
in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system,
where a UAV flies along a planned trajectory to collect computation tasks from
smart devices (SDs). We consider a scenario that SDs are not directly connected
by the base station (BS) and the UAV has two roles to play: MEC server or
wireless relay. The UAV makes task offloading decisions online, in which the
collected tasks can be executed locally on the UAV or offloaded to the BS for
remote processing. The TCTO problem involves multi-objective optimization as
its objectives are to minimize the task delay and the UAV's energy consumption,
and maximize the number of tasks collected by the UAV, simultaneously. This
problem is challenging because the three objectives conflict with each other.
The existing reinforcement learning (RL) algorithms, either single-objective
RLs or single-policy multi-objective RLs, cannot well address the problem since
they cannot output multiple policies for various preferences (i.e. weights)
across objectives in a single run. This paper adapts the evolutionary
multi-objective RL (EMORL), a multi-policy multi-objective RL, to the TCTO
problem. This algorithm can output multiple optimal policies in just one run,
each optimizing a certain preference. The simulation results demonstrate that
the proposed algorithm can obtain more excellent nondominated policies by
striking a balance between the three objectives regarding policy quality,
compared with two evolutionary and two multi-policy RL algorithms.
