# Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks

## TL;DR

This paper introduces a deep reinforcement learning framework for adaptive, decentralized caching in hierarchical content delivery networks, enabling online learning and dynamic adaptation to optimize cache performance.

## Contribution

It proposes a scalable deep Q-network based approach to model and optimize caching decisions in hierarchical networks with unknown policies and dynamic request patterns.

## Key findings

- Achieves significant improvements in caching efficiency.
- Demonstrates adaptability to changing network conditions.
- Validates the approach through numerical simulations.

## Abstract

Caching is envisioned to play a critical role in next-generation content delivery infrastructure, cellular networks, and Internet architectures. By smartly storing the most popular contents at the storage-enabled network entities during off-peak demand instances, caching can benefit both network infrastructure as well as end users, during on-peak periods. In this context, distributing the limited storage capacity across network entities calls for decentralized caching schemes. Many practical caching systems involve a parent caching node connected to multiple leaf nodes to serve user file requests. To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning framework is put forth. To handle the large continuous state space, a scalable deep reinforcement learning approach is pursued. The novel approach relies on a deep Q-network to learn the Q-function, and thus the optimal caching policy, in an online fashion. Reinforcing the parent node with ability to learn-and-adapt to unknown policies of leaf nodes as well as spatio-temporal dynamic evolution of file requests, results in remarkable caching performance, as corroborated through numerical tests.