Rao A. Foundations of Reinforcement Learning with App in Finance 2023

Textbook in PDF format

Reinforcement Learning (RL) is emerging as a practical, powerful technique for solving a variety of complex business problems across industries that involve Sequential Optimal Decisioning under Uncertainty. Although RL is classified as a branch of Machine Learning (ML), it tends to be viewed and treated quite differently from other branches of ML (Supervised and Unsupervised Learning). Indeed, RL seems to hold the key to unlocking the promise of AI—machines that adapt their decisions to vagaries in observed information, while continuously steering towards the optimal outcome. Its penetration in high-profile problems like self-driving cars, robotics and strategy games points to a future where RL algorithms will have decisioning abilities far superior to humans.
But when it comes getting educated in RL, there seems to be a reluctance to jump right in because RL seems to have acquired a reputation of being mysterious and exotic. We often hear even technical people claim that RL involves “advanced math” and “complicated engineering”, and so there seems to be a psychological barrier to entry. While real-world RL algorithms and implementations do get fairly elaborate and complicated in overcoming the proverbial last-mile of business problems, the foundations of RL can actually be learned without heavy technical machinery. A key goal of this book is to demystify RL by finding a balance between A) providing depth of understanding and B) keeping technical content basic.
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