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.