Art

J-GLOBAL ID：201702279965592649   Reference number：17A1207219

Proposal of PSwithEFP and its Evaluation in Multi-Agent Reinforcement Learning

マルチエージェント強化学習におけるPSwithEFPの提案とその評価

Author (3)： MIYAZAKI Kazuteru (National Institution for Academic Degrees and Quality Enhancement of Higher Education, Tokyo, JPN) ,  FURUKAWA Koudai (IHI Transport Machinery Co., Ltd., Tokyo, JPN) ,  KOBAYASHI Hiroaki (Meiji Univ., Kanagawa, JPN)
Material： Journal of Advanced Computational Intelligence and Intelligent Informatics  (J Adv Comput Intell Intell Inform)
Volume： 21  Issue： 5  Page： 930-938  Publication year： Sep. 20, 2017 
JST Material Number： F1398A  ISSN： 1343-0130  Document type： Article
Article type： 原著論文  Country of issue： Japan (JPN)  Language： ENGLISH (EN)

Thesaurus term： *learning ,  *reinforcement learning ,  *dividend ,  failure rate ,  selection
Semi thesaurus term： *multi-agent model learning ,  activity choice ,  開発指向学習 ,  failure probability

JST classification (1)： Artificial intelligence  (JE08000Z)

Reference (30)：

• R. S. Sutton and A. G. Barto, “Reinforcement Learning: An Introduction,” A Bradford Book, MIT Press, 1998.
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• S. Kuroda, K. Miyazaki, and H. Kobayashi, “Introduction of Fixed Mode States into Online Reinforcement Learning with Penalty and Reward and Its Application to Waist Trajectory Generation of Biped Robot,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.6, pp. 758-768, 2013.
• T. Matsui, T. Goto, and K. Izumi, “Acquiring a Government Bond Trading Strategy Using Reinforcement Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.6, pp. 691-696, 2009.
• K. Merrick and M. L. Maher, “Motivated Reinforcement Learning for Adaptive Characters in Open-Ended Simulation Games,” Proc. of the Int. Conf. on Advanced in Computer Entertainment Technology, pp. 127-134, 2007.

Terms in the title (4)： マルチエージェント ,  強化学習 ,  提案 ,  評価