  To improve the quality of computation experience for mobile devices,
mobile-edge computing (MEC) is emerging as a promising paradigm by providing
computing capabilities within radio access networks in close proximity.
Nevertheless, the design of computation offloading policies for a MEC system
remains challenging. Specifically, whether to execute an arriving computation
task at local mobile device or to offload a task for cloud execution should
adapt to the environmental dynamics in a smarter manner. In this paper, we
consider MEC for a representative mobile user in an ultra dense network, where
one of multiple base stations (BSs) can be selected for computation offloading.
The problem of solving an optimal computation offloading policy is modelled as
a Markov decision process, where our objective is to minimize the long-term
cost and an offloading decision is made based on the channel qualities between
the mobile user and the BSs, the energy queue state as well as the task queue
state. To break the curse of high dimensionality in state space, we propose a
deep $Q$-network-based strategic computation offloading algorithm to learn the
optimal policy without having a priori knowledge of the dynamic statistics.
Numerical experiments provided in this paper show that our proposed algorithm
achieves a significant improvement in average cost compared with baseline
policies.
