  With the continuous growth of mobile data and the unprecedented demand for
computing power, resource-constrained edge devices cannot effectively meet the
requirements of Internet of Things (IoT) applications and Deep Neural Network
(DNN) computing. As a distributed computing paradigm, edge offloading that
migrates complex tasks from IoT devices to edge-cloud servers can break through
the resource limitation of IoT devices, reduce the computing burden and improve
the efficiency of task processing. However, the problem of optimal offloading
decision-making is NP-hard, traditional optimization methods are difficult to
achieve results efficiently. Besides, there are still some shortcomings in
existing deep learning methods, e.g., the slow learning speed and the failure
of the original network parameters when the environment changes. To tackle
these challenges, we propose a Deep Meta Reinforcement Learning-based
offloading (DMRO) algorithm, which combines multiple parallel DNNs with
Q-learning to make fine-grained offloading decisions. By aggregating the
perceptive ability of deep learning, the decision-making ability of
reinforcement learning, and the rapid environment learning ability of
meta-learning, it is possible to quickly and flexibly obtain the optimal
offloading strategy from the IoT environment. Simulation results demonstrate
that the proposed algorithm achieves obvious improvement over the Deep
Q-Learning algorithm and has strong portability in making real-time offloading
decisions even in time-varying IoT environments.
