  Mobile Edge Computing (MEC) broadens the scope of computation and storage
beyond the central network, incorporating edge nodes close to end devices. This
expansion facilitates the implementation of large-scale "connected things"
within edge networks. The advent of applications necessitating real-time,
high-quality service presents several challenges, such as low latency, high
data rate, reliability, efficiency, and security, all of which demand
resolution. The incorporation of reinforcement learning (RL) methodologies
within MEC networks promotes a deeper understanding of mobile user behaviors
and network dynamics, thereby optimizing resource use in computing and
communication processes. This paper offers an exhaustive survey of RL
applications in MEC networks, initially presenting an overview of RL from its
fundamental principles to the latest advanced frameworks. Furthermore, it
outlines various RL strategies employed in offloading, caching, and
communication within MEC networks. Finally, it explores open issues linked with
software and hardware platforms, representation, RL robustness, safe RL,
large-scale scheduling, generalization, security, and privacy. The paper
proposes specific RL techniques to mitigate these issues and provides insights
into their practical applications.
