  With the development of Internet of Things (IoT) and communication
technology, the number of next-generation IoT devices has increased
explosively, and the delay requirement for content requests is becoming
progressively higher. Fortunately, the edge-caching scheme can satisfy users'
demands for low latency of content. However, the existing caching schemes are
not smart enough. To address these challenges, we propose a human-like hybrid
caching architecture based on the software defined edge cloud, which
simultaneously considers the content popularity and the fine-grained user
characteristics. Then, an optimization problem with a caching hit ratio as an
optimization objective is formulated. To solve this problem, using
reinforcement learning, we design a human-like hybrid caching algorithm.
Extensive experiments show that compared with popular caching schemes,
human-like hybrid caching schemes can improve the cache hit ratio by 20%.
