Behavior learning of autonomous agents in continuous state using function approximation

Min Kyu Shon, Junichi Murata

研究成果: 書籍/レポート タイプへの寄稿

抄録

This paper presents a method for behavior learning of an autonomous agent using modified Learning Vector Quantization (LVQ) with fuzzy sets in continuous state space. When the environment is a continuous state space, it has infinitely many state values. So, it is impossible to learn a good action to take in each of the state values. This paper uses a function approximation technique based on the LVQ algorithm to learn actions of agent in continuous state space. An advantage of this technique is that it can represent the mapping between the continuous-valued state space and appropriate actions with a finite number of parameters. An example illustrates its validity in continuous space problems.

本文言語英語
ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
編集者Mircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
出版社Springer Verlag
ページ1213-1219
ページ数7
ISBN(印刷版)9783540301325
DOI
出版ステータス出版済み - 2004

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3213
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

!!!All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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