  Utilizing unmanned aerial vehicles (UAVs) with edge server to assist
terrestrial mobile edge computing (MEC) has attracted tremendous attention.
Nevertheless, state-of-the-art schemes based on deterministic optimizations or
single-objective reinforcement learning (RL) cannot reduce the backlog of task
bits and simultaneously improve energy efficiency in highly dynamic network
environments, where the design problem amounts to a sequential decision-making
problem. In order to address the aforementioned problems, as well as the curses
of dimensionality introduced by the growing number of terrestrial terrestrial
users, this paper proposes a distributed multi-objective (MO) dynamic
trajectory planning and offloading scheduling scheme, integrated with MORL and
the kernel method. The design of n-step return is also applied to average
fluctuations in the backlog. Numerical results reveal that the n-step return
can benefit the proposed kernel-based approach, achieving significant
improvement in the long-term average backlog performance, compared to the
conventional 1-step return design. Due to such design and the kernel-based
neural network, to which decision-making features can be continuously added,
the kernel-based approach can outperform the approach based on fully-connected
deep neural network, yielding improvement in energy consumption and the backlog
performance, as well as a significant reduction in decision-making and online
learning time.
