Foundations of reinforcement learning with applications in finance [electronic resource]
- 作者： Rao, Ashwin, author.
- 其他作者：
- 出版： Boca Raton, FL : CRC Press
- 版本：First edition.
- 主題： Finance--Study and teaching. , Reinforcement learning. , Finances--Etude et enseignement. , Apprentissage par renforcement (Intelligence artificielle) , MATHEMATICS / Applied , BUSINESS & ECONOMICS / Finance , Finance--Study and teaching , Reinforcement learning
- ISBN： 9781003229193 (electronic bk.) 、 1003229190 (electronic bk.) 、 9781000801057 (electronic bk.) 、 1000801055 (electronic bk.) 、 9781000801101 (electronic bk.) 、 1000801101 (electronic bk.)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: Includes bibliographical references and index.
- 摘要註: "Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas - especially finance. Reinforcement Learning is emerging as a viable and powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and exotic. Even technical people will often claim that the subject involves "advanced math" and "complicated engineering", erecting a psychological barrier to entry against otherwise interested students. This book seeks to overcome that barrier, and to introduce the foundations of Reinforcement Learning in a way that balances depth of understanding with clear, minimally technical delivery. Features Focus on the foundational theory underpinning Reinforcement Learning Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or industry specialists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding"--
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讀者標籤：
- 系統號： 005529743 | 機讀編目格式
館藏資訊
Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas -- especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans. But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms Suitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related courses Suitable for a professional audience of quantitative analysts or data scientists Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding.