# Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks

arXiv: https://arxiv.org/abs/1905.05256 · HTML: https://mlsanity.com/paper/1905.05256/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 3 of 3)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data](https://mlsanity.com/paper/2203.12674/) (2022)
- [Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation](https://mlsanity.com/paper/2205.13944/) (2022)

## Abstract (arXiv)

The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques. Motivated by this, we in this paper consider edge caching at the base stations with unknown content popularity distributions. To solve the dynamic control problem of making caching decisions, we propose a deep actor-critic reinforcement learning based multi-agent framework with the aim to minimize the overall average transmission delay. To evaluate the proposed framework, we compare the learning-based performance with three other caching policies, namely least recently used (LRU), least frequently used (LFU), and first-in-first-out (FIFO) policies. Through simulation results, performance improvements of the proposed framework over these three caching algorithms have been identified and its superior ability to adapt to varying environments is demonstrated.