A new approach for supervised learning based influence value reinforcement learning

André Valdivia, Jose Alfredo Herrera Quispe, Dennis Barrios-Aranibar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The neural self-organization, is an innate feature of the mammal's brains, and is necessary for its operation. The most known neuronal models that use this characteristic are the self-organized maps (SOM) and the adaptive resonance theory (ART), but those models, did not take the neuron as a processing unit, as the biological counterpart. On the other hand, the influence value learning paradigm [1], used in multi-agent environments, proof that agents can communicate with each other [2]; and they can self-organize to assign tasks; without any interference. Motivated by this missing feature in artificial networks, and with the influence value reinforcement learning algorithm; a new approach to supervised learning was modeled using the neuron as an agent learning by reinforcement.

Original languageEnglish
Title of host publication2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018
PublisherAssociation for Computing Machinery
Pages24-28
Number of pages5
ISBN (Electronic)9781450363365
DOIs
StatePublished - 2 Feb 2018
Event2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018 - Phu Quoc Island, Viet Nam
Duration: 2 Feb 20184 Feb 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd International Conference on Machine Learning and Soft Computing, ICMLSC 2018
Country/TerritoryViet Nam
CityPhu Quoc Island
Period2/02/184/02/18

Bibliographical note

Publisher Copyright:
© Association for Computing Machinery. All rights reserved.

Keywords

  • Multi-agent
  • Neural networks
  • Reinforcement learning

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