Reinforcement learning on a futures market simulator

Koichi Moriyama, Mitsuhiro Matsumoto, Ken Ichi Fukui, Satoshi Kurihara, Masayuki Numao

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

1 Citation (Scopus)

Abstract

In recent years, it becomes vigorous to forecast a market by using machine learning methods. Since they assume that each trader's individual decisions do not affect market prices at all, most existing works use a past market data set. Meanwhile there is an attempt to analyze economic phenomena by constructing a virtual market simulator, where human and artificial traders really make trades. Since prices in the market are determined by every trader's decisions, it is more realistic and the assumption cannot be applied any more. In this work, we design and evaluate several reinforcement learners on a futures market simulator U-Mart (Unreal Market as an Artificial Research Testbed). After that, we compare our learner to the previous champions of U-Mart competitions.

Original languageEnglish
Title of host publicationAgent and Multi-Agent Systems
Subtitle of host publicationTechnologies and Applications - First KES International Symposium, KES-AMSTA 2007, Proceedings
PublisherSpringer Verlag
Pages42-52
Number of pages11
ISBN (Print)9783540728290
DOIs
Publication statusPublished - 2007 Jan 1
Externally publishedYes
Event1st KES International Symposium on Agent and Multi-Agent Systems - Technologies and Applications, KES-AMSTA 2007 - Wroclaw, Poland
Duration: 2007 May 312007 Jun 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4496 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st KES International Symposium on Agent and Multi-Agent Systems - Technologies and Applications, KES-AMSTA 2007
CountryPoland
CityWroclaw
Period07/5/3107/6/1

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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