  In this paper, we consider deploying multiple Unmanned Aerial Vehicles (UAVs)
to enhance the computation service of Mobile Edge Computing (MEC) through
collaborative computation among UAVs. In particular, the tasks of different
types and service requirements in MEC network are offloaded from one UAV to
another. To pursue the goal of low-carbon edge computing, we study the problem
of minimizing system energy consumption by jointly optimizing computation
resource allocation, task scheduling, service placement, and UAV trajectories.
Considering the inherent unpredictability associated with task generation and
the dynamic nature of wireless fading channels, addressing this problem
presents a significant challenge. To overcome this issue, we reformulate the
complicated non-convex problem as a Markov decision process and propose a soft
actor-critic-based trajectory optimization and resource allocation algorithm to
implement a flexible learning strategy. Numerical results illustrate that
within a multi-UAV-enabled MEC network, the proposed algorithm effectively
reduces the system energy consumption in heterogeneous tasks and services
scenarios compared to other baseline solutions.
