Foundations of Reinforcement Learning with Applications in Finance
暫譯: 強化學習基礎與金融應用

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: $3,610
  • 貴賓價: 9.5 折 $3,430
  • 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，並使其成為在應用領域（特別是金融）學習和工作的人的實用工具。

強化學習正在成為解決各行各業中涉及不確定性下的序列最佳決策的各種複雜問題的強大技術。它在自駕車、機器人技術和策略遊戲等高知名度問題中的應用，預示著未來強化學習算法將擁有遠超人類的決策能力。然而，在這個領域獲得教育似乎存在著一種不情願的情緒，因為強化學習似乎已經獲得了神秘和技術挑戰的聲譽。

本書努力通過強調基礎數學和在精心設計的 Python 代碼中實現模型和算法，來 impart 清晰且深刻的理解，並對幾個可以用強化學習解決的金融交易問題進行全面的探討。本書是在多年對這些主題的教學進行反覆實驗後創作的，適用於大學學生和行業從業者。

特色

- 專注於強化學習的基礎理論及相應模型和算法的軟體設計
- 適合作為強化學習課程的主要教材，也可作為應用/金融數學、程式設計及其他相關課程的補充閱讀
- 適合量化分析師或數據科學家的專業讀者
- 融合理論/數學、程式設計/算法和現實世界的金融細微差別，同時始終努力保持簡單性並建立直觀理解。

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，這是一家電子商務公司，他和他的團隊為供應鏈和物流、商品銷售、行銷、搜尋、個人化、定價和客戶服務開發數學模型和演算法。Ashwin 同時也是史丹佛大學的兼任教授，專注於隨機控制領域的研究和教學，特別是強化學習演算法在金融和零售中的應用。之前，Ashwin 曾擔任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與工程學士學位，以及南加州大學的計算機科學博士學位，專攻演算法理論和抽象代數。

Tikhon Jelvis 是一位程式設計師，專注於將程式語言和函數式程式設計的理念應用於機器學習和數據科學。他曾擔任 Target 的首席科學家，開發庫存優化、模擬和需求預測系統，並且是 Haskell 社群的演講者和開源貢獻者，擔任 Haskell.org 董事會成員。

類似商品