Adopting Deep Reinforcement Learning in Edge Caching Strategy

The increasing demand for digital content is putting a strain on network infrastructures, prompting a shift towards edge server architecture, which aims to decentralize data processing and reduce content delivery latency. This study investigates edge server caching optimization using Deep Reinforcement Learning (DRL). The motivation stems from the need to alleviate the burden on origin servers and to expedite content delivery. An edge server locates closer to the users in a network rather than relying on a centralized server. DRL contains an agent learns to make decisions through trial and error. Experiments conducted across different simulated user request patterns show that modifications can enhance cache hit rates.