  Wireless network optimization has been becoming very challenging as the
problem size and complexity increase tremendously, due to close couplings among
network entities with heterogeneous service and resource requirements. By
continuously interacting with the environment, deep reinforcement learning
(DRL) provides a mechanism for different network entities to build knowledge
and make autonomous decisions to improve network performance. In this article,
we first review typical DRL approaches and recent enhancements. We then discuss
the applications of DRL for mobile edge computing (MEC), which can be used for
the low-power IoT devices, e.g., wireless sensors in healthcare monitoring, to
offload computation workload to nearby MEC servers. To balance power
consumption in offloading and computation, we propose a novel hybrid offloading
model that exploits the complement operations of RF communications and
low-power backscatter communications. The DRL framework is then customized to
optimize the transmission scheduling and workload allocation in two
communications technologies, which is shown to enhance the offloading
performance significantly compared with existing schemes.
