ABSTRACT
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
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To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book
TABLE OF CONTENTS
chapter Chapter 1|18 pages
Overview
chapter Chapter 2|20 pages
Programming and Design
part Module I|132 pages
Processes and Planning Algorithms
chapter 40Chapter 3|32 pages
Markov Processes
chapter Chapter 4|30 pages
Markov Decision Processes
chapter Chapter 5|36 pages
Dynamic Programming Algorithms
chapter Chapter 6|32 pages
Function Approximation and Approximate Dynamic Programming
part Module II|106 pages
Modeling Financial Applications
chapter 172Chapter 7|12 pages
Utility Theory
chapter Chapter 8|22 pages
Dynamic Asset-Allocation and Consumption
chapter Chapter 9|36 pages
Derivatives Pricing and Hedging
chapter Chapter 10|34 pages
Order-Book Trading Algorithms
part Module III|132 pages
Reinforcement Learning Algorithms
chapter 278Chapter 11|36 pages
Monte-Carlo and Temporal-Difference for Prediction
chapter Chapter 12|34 pages
Monte-Carlo and Temporal-Difference for Control
chapter Chapter 13|32 pages
Batch RL, Experience-Replay, DQN, LSPI, Gradient TD
chapter Chapter 14|28 pages
Policy Gradient Algorithms
part Module IV|50 pages
Finishing Touches