Foundations of Reinforcement Learning with Applications in Finance

Rao, Ashwin, Jelvis, Tikhon

Foundations of Reinforcement Learning with Applications in Finance
  • 出版商: CRC
  • 出版日期: 2022-12-16
  • 售價: $4,070
  • 貴賓價: 9.5 折 $3,867
  • 語言: 英文
  • 頁數: 500
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032124121
  • ISBN-13: 9781032124124
  • 相關分類: Reinforcement、DeepLearning

    • 海外代購書籍(需單獨結帳)

商品描述

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.

商品描述(中文翻譯)

《強化學習基礎與金融應用》旨在揭開強化學習的神秘面紗，並將其變成一個對於研究和應用領域（尤其是金融領域）的實用工具。

強化學習正逐漸成為解決各種涉及不確定性下的順序最佳決策的複雜問題的強大技術。它在自駕車、機器人和策略遊戲等重要問題中的應用，預示著未來強化學習算法將具有遠超人類的決策能力。然而，當涉及到學習這個領域時，人們似乎不太願意投入其中，因為強化學習似乎已經被視為神秘和技術上具有挑戰性。

本書力求通過強調基礎數學知識並使用設計良好的Python代碼來實現模型和算法，以及對可以通過強化學習解決的多個金融交易問題的全面覆蓋，來傳授對這一主題的清晰而深入的理解。本書是在多年的教授大學生和業界從業人員這些主題的教學實踐中進行迭代實驗後創作而成。

特點：

- 關注強化學習的基礎理論和相應模型和算法的軟件設計
- 適合作為強化學習課程的主要教材，也適合應用/金融數學、編程和其他相關課程的補充閱讀
- 適合量化分析師或數據科學家等專業觀眾
- 在保持簡單性和建立直觀理解的同時，融合了理論/數學、編程/算法和現實世界的金融細微差異。

作者簡介

Ashwin Rao is the Chief Science Officer of Wayfair, an e-commerce company where he and his team develop mathematical models and algorithms for supply-chain and logistics, merchandising, marketing, search, personalization, pricing and customer service. Ashwin is also an Adjunct Professor at Stanford University, focusing his research and teaching in the area of Stochastic Control, particularly Reinforcement Learning algorithms with applications in Finance and Retail. Previously, Ashwin was a Managing Director at Morgan Stanley and a Trading Strategist at Goldman Sachs. Ashwin holds a Bachelor's degree in Computer Science and Engineering from IIT-Bombay and a Ph.D in Computer Science from University of Southern California, where he specialized in Algorithms Theory and Abstract Algebra.

Tikhon Jelvis is a programmer who specializes in bringing ideas from programming languages and functional programming to machine learning and data science. He has developed inventory optimization, simulation and demand forecasting systems as a Principal Scientist at Target and is a speaker and open-source contributor in the Haskell community where he serves on the board of directors for Haskell.org.

作者簡介(中文翻譯)

Ashwin Rao是Wayfair的首席科學官，Wayfair是一家電子商務公司，他和他的團隊開發供應鏈和物流、商品銷售、市場營銷、搜索、個性化、定價和客戶服務的數學模型和算法。Ashwin還是斯坦福大學的兼職教授，他的研究和教學專注於隨機控制領域，特別是在金融和零售領域應用強化學習算法。在此之前，Ashwin曾是摩根士丹利的董事總經理和高盛的交易策略師。Ashwin擁有印度理工學院孟買分校的計算機科學和工程學士學位，以及南加州大學的計算機科學博士學位，專攻算法理論和抽象代數。

Tikhon Jelvis是一位專注於將編程語言和函數式編程思想應用於機器學習和數據科學的程序員。作為Target的首席科學家，他開發了庫存優化、模擬和需求預測系統。他還是Haskell社區的演講者和開源貢獻者，在Haskell.org的董事會任職。

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