Coordination in multiagent reinforcement learning systems

M. A.S. Kamal, Junichi Murata

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

This paper presents a novel method for real-time coordination control of multiagent systems in maximizing global benefits keeping a balance with individual benefits of agents. In this coordination mechanism a reinforcement-learning agent learns to select its action estimating global state value and immediate reward. The estimated global state value of the system makes an agent cooperative with others. This learning method is implemented in the test bed multiagent transportation service control for a city. The outstanding performance of the proposed. method in different aspects compared to other heuristic methods indicates its effectiveness for multiagent cooperative systems.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Verlag
Pages1197-1204
Number of pages8
ISBN (Print)9783540301325
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3213
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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