  This letter studies a basic wireless caching network where a source server is
connected to a cache-enabled base station (BS) that serves multiple requesting
users. A critical problem is how to improve cache hit rate under dynamic
content popularity. To solve this problem, the primary contribution of this
work is to develop a novel dynamic content update strategy with the aid of deep
reinforcement learning. Considering that the BS is unaware of content
popularities, the proposed strategy dynamically updates the BS cache according
to the time-varying requests and the BS cached contents. Towards this end, we
model the problem of cache update as a Markov decision process and put forth an
efficient algorithm that builds upon the long short-term memory network and
external memory to enhance the decision making ability of the BS. Simulation
results show that the proposed algorithm can achieve not only a higher average
reward than deep Q-network, but also a higher cache hit rate than the existing
replacement policies such as the least recently used, first-in first-out, and
deep Q-network based algorithms.
