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 broadens the scope of computation and storage beyond the central network%2C 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%2C high-quality service presents several challenges%2C such as low latency%2C high data rate%2C reliability%2C efficiency%2C and security%2C 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%2C thereby optimizing resource use in computing and communication processes. This paper offers an exhaustive survey of RL applications in MEC networks%2C initially presenting an overview of RL from its fundamental principles to the latest advanced frameworks. Furthermore%2C it outlines various RL strategies employed in offloading%2C caching%2C and communication within MEC networks. Finally%2C it explores open issues linked with software and hardware platforms%2C representation%2C RL robustness%2C safe RL%2C large-scale scheduling%2C generalization%2C security%2C and privacy. The paper proposes specific RL techniques to mitigate these issues and provides insights into their practical applications.?quality=80&w=800)
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