  Mobile networks are experiencing tremendous increase in data volume and user
density. An efficient technique to alleviate this issue is to bring the data
closer to the users by exploiting the caches of edge network nodes, such as
fixed or mobile access points and even user devices. Meanwhile, the fusion of
machine learning and wireless networks offers a viable way for network
optimization as opposed to traditional optimization approaches which incur high
complexity, or fail to provide optimal solutions. Among the various machine
learning categories, reinforcement learning operates in an online and
autonomous manner without relying on large sets of historical data for
training. In this survey, reinforcement learning-aided mobile edge caching is
presented, aiming at highlighting the achieved network gains over conventional
caching approaches. Taking into account the heterogeneity of sixth generation
(6G) networks in various wireless settings, such as fixed, vehicular and flying
networks, learning-aided edge caching is presented, departing from traditional
architectures. Furthermore, a categorization according to the desirable
performance metric, such as spectral, energy and caching efficiency, average
delay, and backhaul and fronthaul offloading is provided. Finally, several open
issues are discussed, targeting to stimulate further interest in this important
research field.
