Research progress and prospects of deep reinforcement learning in the field of mobile edge computing

In the rapidly evolving landscape of Mobile Edge Computing (MEC), Deep Reinforcement Learning (DRL) emerges as a potent solution for task offloading, resource allocation, and network optimization challenges due to its exceptional decision-making and adaptability features. This paper commences by reviewing key technologies and challenges within MEC and proceeds to analyze the current applications and breakthroughs DRL has fostered in this realm. Subsequent sections present empirical research and data analyses to showcase the performance of various DRL algorithms in MEC settings, laying a quantitative foundation for further investigative directions. Synthesizing extant research, the paper concludes by discussing open issues and future prospects of DRL in the MEC domain, offering insights and projections for related studies.