Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground
  Cooperative MEC
Yang Huang Miaomiao Dong Yijie Mao Wenqiang Liu Zhen Gao
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.