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 viable and 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 exotic. Even technical people will often claim that the subject involves advanced math and complicated engineering, erecting a psychological barrier to entry against otherwise interested students.
This book seeks to overcome that barrier, and to introduce the foundations of Reinforcement Learning in a way that balances depth of understanding with clear, minimally technical delivery.
- Focus on the foundational theory underpinning Reinforcement Learning
- 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 industry specialists
- Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding.