Emergence of information processor using real world - Real-time learning of pursuit problem-

Hiroyuki Fujii, Kazuyuki Ito, Akio Gofuku

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Real-time reinforcement learning is difficult because number of trials is too much to complete learning within limited time. To solve the problem, we consider reduction of action-state space by information processor using real world without prior knowledge. We obtain the information processor in evolution by setting the fitness as ease of learning. As a typical example, we address pursuit problem in which dynamics is regarded. As a result, the processor has been obtained in evolution and agent has learned in real-time.

Original languageEnglish
Title of host publicationProceedings - Sixth International Conference on Hybrid Intelligent Systems and Fourth Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006
DOIs
Publication statusPublished - Dec 1 2006
Event6th International Conference on Hybrid Intelligent Systems and 4th Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006 - Auckland, New Zealand
Duration: Dec 13 2006Dec 15 2006

Publication series

NameProceedings - Sixth International Conference on Hybrid Intelligent Systems and Fourth Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006

Other

Other6th International Conference on Hybrid Intelligent Systems and 4th Conference on Neuro-Computing and Evolving Intelligence, HIS-NCEI 2006
Country/TerritoryNew Zealand
CityAuckland
Period12/13/0612/15/06

ASJC Scopus subject areas

  • Computer Science(all)

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