  In this paper, we investigate mobile edge computing (MEC) networks for
intelligent internet of things (IoT), where multiple users have some
computational tasks assisted by multiple computational access points (CAPs). By
offloading some tasks to the CAPs, the system performance can be improved
through reducing the latency and energy consumption, which are the two
important metrics of interest in the MEC networks. We devise the system by
proposing the offloading strategy intelligently through the deep reinforcement
learning algorithm. In this algorithm, Deep Q-Network is used to automatically
learn the offloading decision in order to optimize the system performance, and
a neural network (NN) is trained to predict the offloading action, where the
training data is generated from the environmental system. Moreover, we employ
the bandwidth allocation in order to optimize the wireless spectrum for the
links between the users and CAPs, where several bandwidth allocation schemes
are proposed. In further, we use the CAP selection in order to choose one best
CAP to assist the computational tasks from the users. Simulation results are
finally presented to show the effectiveness of the proposed reinforcement
learning offloading strategy. In particular, the system cost of latency and
energy consumption can be reduced significantly by the proposed deep
reinforcement learning based algorithm.
