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Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
West Ashley Library
9 a.m. - 7 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. - 8 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 1 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. - 8 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. - 8 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. - 8 p.m.
Phone: (843) 849-6161
McClellanville Library
9 a.m. - 6 p.m.
Phone: (843) 887-3699
Keith Summey North Charleston Library
9 a.m. - 8 p.m.
Phone: (843) 744-2489
John's Island Library
9 a.m. - 8 p.m.
Phone: (843) 559-1945
Hurd/St. Andrews Library
9 a.m. - 8 p.m.
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Folly Beach Library
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Miss Jane's Building (Edisto Library Temporary Location)
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Dorchester Road Library
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Phone: (843) 552-6466
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9 a.m. - 7 p.m.
Phone: (843) 722-7550
Baxter-Patrick James Island
9 a.m. - 8 p.m.
Phone: (843) 795-6679
Main Library
9 a.m. - 8 p.m.
Phone: (843) 805-6930
Bees Ferry West Ashley Library
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Phone: (843) 805-6892
Mobile Library
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Phone: (843) 805-6909
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Reinforcement Learning for Quantitative Trading.
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- Author(s): SHUO SUN; RUNDONG WANG; BO AN
- Source:
ACM Transactions on Intelligent Systems & Technology; Jun2023, Vol. 14 Issue 3, p1-29, 29p- Subject Terms:
- Source:
- Additional Information
- Abstract: Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques' potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of ACM Transactions on Intelligent Systems & Technology is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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