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A Multi-agent Q-learning Framework for Optimizing Stock Trading Systems Jae Won Lee1 and Jangmin O2 1 School of Computer Science and Engineering, Sungshin Women’s University,
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Document Date: 2003-08-08 06:11:02

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File Size: 421,01 KB

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City

Cambridge / Oxford / /

Company

SNN / J. Sa / Neural Information Processing Systems / BP / Optimizing Stock Trading Systems / Neural Networks / MIT Press / Baird L. C. / Y. Sa / MAQ / KOSPI / Industrial Electronics / Kendall / /

Country

Jordan / Korea / /

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Facility

Seoul National University / Sungshin Women’s University / /

IndustryTerm

stock trading systems / risk management / intelligent decision support systems / conventional supervised learning algorithm / search space / learning algorithm / /

MarketIndex

S&P 500 / KOSPI 200 / /

Organization

Seoul National University / Seoul / Sungshin Women’s University / Seoul / MIT / School of Computer Science and Engineering / School of Computer Engineering / /

Person

Jae Won / Morgan Kaufmann / /

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Position

rt / /

Product

iRiver E100 Portable Audio Device / /

ProvinceOrState

New York / /

PublishedMedium

Machine Learning / /

Technology

Stochastic Iterative Dynamic Programming Algorithms / neural network / conventional supervised learning algorithm / machine learning / Qlearning algorithm / detailed learning algorithm / Q-learning algorithm / 3.3 Learning Algorithm / /

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