A Distributed Reinforcement Learning Scheme for Network Routing

Abstract

In this paper we describe a self-adjusting algorithm for packet routing, ill which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used to keep accurate statistics at each node on which routing policies lead to minimal delivery times, In simple experiments involving a 36-node, irregularly connected network, this learning approach proves superior to a nonadaptive algorithm based on precomputed shortest paths.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1993
Accession Number
ADA270600

Entities

People

  • Justin Boyan
  • Michael L. Littman

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Communication Networks
  • Communication Systems
  • Computer Science
  • Control Systems
  • Distance Learning
  • Learning
  • Local Area Networks
  • Network Science
  • Network Topology
  • Networks
  • Neural Networks
  • Reinforcement Learning
  • Simulations
  • Simulators
  • Topology

Fields of Study

  • Computer science

Readers

  • Graph Algorithms and Convex Optimization.
  • Neural Network Machine Learning.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks