Learning to Code: Coded Caching via Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning approach to optimize coded caching in networks, reducing delivery delay and computational complexity compared to existing methods.

Contribution

It proposes a novel deep reinforcement learning algorithm for coded caching that adapts to arbitrary cache loads, outperforming traditional strategies in delay and efficiency.

Findings

Learned coded delivery strategy slightly outperforms state-of-the-art in delay

Significantly reduces computational complexity

Demonstrates effectiveness through simulation results

Abstract

We consider a system comprising a file library and a network with a server and multiple users equipped with cache memories. The system operates in two phases: a prefetching phase, where users load their caches with parts of contents from the library, and a delivery phase, where users request files from the library and the server needs to send the uncached parts of the requested files to the users. For the case where the users' caches are arbitrarily loaded, we propose an algorithm based on deep reinforcement learning to minimize the delay of delivering requested contents to the users in the delivery phase. Simulation results demonstrate that our proposed deep reinforcement learning agent learns a coded delivery strategy for sending the requests to the users, which slightly outperforms the state-of-the-art performance in terms of delivery delay, while drastically reducing the computational complexity.