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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 reinforcement learning (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.

Reconfigurable intelligent surface (RIS)-aided wireless communications is a hot research topic in academic and industry communities since it can enhance both the spectrum and energy efficiency of wireless systems by artificially reconfiguring the wireless propagation environment. An RIS can configure tiny antenna elements or scatterers, which can be judiciously tuned to enhance the signal power for desired users, such as primary users in cognitive radio networks, or suppress the signal power for undesired users, such as eavesdroppers for physical layer security. RISs also find promising applications in dense urban areas or indoor scenarios, where the electromagnetic waves are prone to be blocked by obstacles such as buildings and walls. There are numerous advantages associated with RISs. For instance, since an RIS needs no analog-to-digital converters or radiofrequency chains, it reduces the power consumption, hence improving sustainability and reducing cost. RISs can be fabricated at a limited complexity and cost, and they can be easily deployed on buildings facades, walls, ceilings, and streetlamps. Furthermore, RISs can be readily integrated into current wireless networks (both cellular networks and WiFi). Due to these appealing advantages, RIS is envisioned to be one of the key technologies for the sixth-generation (6G) wireless networks.