E-Books →Foundations of Reinforcement Learning with Applications in Finance

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 algorithmsSuitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related coursesSuitable for a professional audience of quantitative analysts or data scientistsBlends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding.