Research on intelligent caching strategy of content distribution network based on deep reinforcement learning

This study proposes an intelligent caching strategy for content distribution network (CDN) based on deep reinforcement learning, aiming to optimize cache hit rate and reduce access delay. The cache problem is modeled by Markov decision process, and the deep Q learning (DQN) algorithm is designed. The action value function is approximated by neural network to realize dynamic cache decision. Experimental results show that the model is significantly better than traditional LRU and LFU algorithms in terms of cache hit rate and access delay, especially in high-load scenarios. This study provides a new technical path for CDN performance optimization and expands the application of deep reinforcement learning in the field of network optimization.