  With the tremendous growth of data traffic over wired and wireless networks
along with the increasing number of rich-media applications, caching is
envisioned to play a critical role in next-generation networks. To
intelligently prefetch and store contents, a cache node should be able to learn
what and when to cache. Considering the geographical and temporal content
popularity dynamics, the limited available storage at cache nodes, as well as
the interactive in uence of caching decisions in networked caching settings,
developing effective caching policies is practically challenging. In response
to these challenges, this chapter presents a versatile reinforcement learning
based approach for near-optimal caching policy design, in both single-node and
network caching settings under dynamic space-time popularities. The herein
presented policies are complemented using a set of numerical tests, which
showcase the merits of the presented approach relative to several standard
caching policies.
