  Mobile edge computing (MEC) is considered a novel paradigm for
computation-intensive and delay-sensitive tasks in fifth generation (5G)
networks and beyond. However, its uncertainty, referred to as dynamic and
randomness, from the mobile device, wireless channel, and edge network sides,
results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization
problems. Thanks to the evolved reinforcement learning (RL), upon iteratively
interacting with the dynamic and random environment, its trained agent can
intelligently obtain the optimal policy in MEC. Furthermore, its evolved
versions, such as deep RL (DRL), can achieve higher convergence speed
efficiency and learning accuracy based on the parametric approximation for the
large-scale state-action space. This paper provides a comprehensive research
review on RL-enabled MEC and offers insight for development in this area. More
importantly, associated with free mobility, dynamic channels, and distributed
services, the MEC challenges that can be solved by different kinds of RL
algorithms are identified, followed by how they can be solved by RL solutions
in diverse mobile applications. Finally, the open challenges are discussed to
provide helpful guidance for future research in RL training and learning MEC.
