Using Collective Intelligence to Route Internet Traffic
David H. Wolpert, Kagan Tumer, Jeremy Frank

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.
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.
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · Game Theory and Applications · Neural Networks and Reservoir Computing
