# Using Collective Intelligence to Route Internet Traffic

## TL;DR

This paper introduces Collective Intelligence (COIN), a set of reinforcement learning algorithms designed to optimize internet traffic routing, demonstrating superior performance over existing RL-based shortest path algorithms.

## Contribution

The paper develops the theory of COINs and applies it to internet traffic routing, showing improved results over prior RL-based methods.

## Key findings

- COINs outperform previous RL-based routing algorithms
- Experiments demonstrate improved traffic routing efficiency
- Theoretical framework supports automated design of collective RL systems

## Abstract

A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed in an automated fashion so that their collective behavior optimizes a global utility function. We summarize the theory of COINs, then present experiments using that theory to design COINs to control internet traffic routing. These experiments indicate that COINs outperform all previously investigated RL-based, shortest path routing algorithms.