論文

査読有り
2009年3月

Reinforcement-learning agents with different temperature parameters explain the variety of human action-selection behavior in a Markov decision process task

NEUROCOMPUTING
  • Fumihiko Ishida
  • ,
  • Takahiro Sasaki
  • ,
  • Yutaka Sakaguchi
  • ,
  • Hiroyuki Shimai

72
7-9
開始ページ
1979
終了ページ
1984
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.neucom.2008.04.009
出版者・発行元
ELSEVIER SCIENCE BV

We investigated the characteristics of the human action-selection in performing a Markov decision process (MDP) task, and compared them to those of reinforcement-learning (RL) agents. The behavior of human participants was roughly classified into two qualitatively different types. On the other hand, surprisingly, the variety of human behavior could be explained simply by a single parameter of the degree of randomness (i.e., the temperature parameter) in the action-selection rules of the RL agents. This result implies that the various behaviors of human action-selection may be determined by a simple mechanism in the brain. (c) 2008 Elsevier B.V. All rights reserved.

リンク情報
DOI
https://doi.org/10.1016/j.neucom.2008.04.009
J-GLOBAL
https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201302259451895166
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000264993200062&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.neucom.2008.04.009
  • ISSN : 0925-2312
  • J-Global ID : 201302259451895166
  • Web of Science ID : WOS:000264993200062

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