UAV Assisted Cooperative Caching on Network Edge Using Multi-Agent Actor-Critic Reinforcement Learning

In recent times, caching at edge nodes is a well-known technique to overcome the limitation of strict latency, which simultaneously improves users' Quality of Experience (QoE). However, choosing an appropriate caching policy and content placement poses another significant issue that has been acknowledged in this research. Conventional caching policies that are experimented with at the edge do not consider the dynamic and stochastic characteristics of edge caching. As a result, we have proposed a cooperative deep reinforcement learning algorithm that deals with the dynamic nature of content demand. It also ensures efficient use of storage through the cooperation between nodes. In addition, previous works on cooperative caching have assumed the users to be static and didn't consider the mobile nature…