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Foundations of Reinforcement Learning with Applications in Finance (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)
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Price: $61.07
(as of Dec 24,2024 11:37:13 UTC – Details)
ASIN : B0BS1C266D
Publisher : Chapman and Hall/CRC; 1st edition (December 16, 2022)
Publication date : December 16, 2022
Language : English
File size : 22977 KB
Simultaneous device usage : Up to 4 simultaneous devices, per publisher limits
Text-to-Speech : Not enabled
Enhanced typesetting : Not Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 522 pages
Format : Print Replica
Foundations of Reinforcement Learning with Applications in Finance (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)
Reinforcement learning is a powerful machine learning technique that has gained popularity in recent years due to its ability to learn complex behaviors and make decisions in an uncertain environment. In the field of finance, reinforcement learning has been successfully applied to a variety of problems, including portfolio optimization, algorithmic trading, and risk management.
In this book, the authors provide a comprehensive introduction to the foundations of reinforcement learning, with a focus on its applications in finance. The book covers key concepts such as Markov decision processes, dynamic programming, and temporal difference learning, as well as more advanced topics such as deep reinforcement learning and policy gradient methods.
Throughout the book, the authors provide practical examples and case studies to illustrate how reinforcement learning can be used to solve real-world financial problems. Whether you are a student, researcher, or practitioner in the field of finance, this book is a valuable resource for understanding the principles of reinforcement learning and applying them to financial decision-making.
With its clear explanations, rigorous mathematical treatment, and practical examples, Foundations of Reinforcement Learning with Applications in Finance is a must-read for anyone interested in harnessing the power of reinforcement learning in the financial industry.
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