A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

Lincan Li, Chiew Foong Kwong, Qianyu Liu, Jing Wang

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)
28 Downloads (Pure)


This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%-64% improved in terms of the cache hit ratio and 56%-59% decreased in terms of the average latency.

Original languageEnglish
Article number8836592
JournalWireless Communications and Mobile Computing
Publication statusPublished - 2020

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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