  Multi-access edge computing (MEC) aims to extend cloud service to the network
edge to reduce network traffic and service latency. A fundamental problem in
MEC is how to efficiently offload heterogeneous tasks of mobile applications
from user equipment (UE) to MEC hosts. Recently, many deep reinforcement
learning (DRL) based methods have been proposed to learn offloading policies
through interacting with the MEC environment that consists of UE, wireless
channels, and MEC hosts. However, these methods have weak adaptability to new
environments because they have low sample efficiency and need full retraining
to learn updated policies for new environments. To overcome this weakness, we
propose a task offloading method based on meta reinforcement learning, which
can adapt fast to new environments with a small number of gradient updates and
samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the
offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To
efficiently train the seq2seq network, we propose a method that synergizes the
first order approximation and clipped surrogate objective. The experimental
results demonstrate that this new offloading method can reduce the latency by
up to 25% compared to three baselines while being able to adapt fast to new
environments.
