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。

Features



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。