
Автор: Ashwin Rao, Tikhon Jelvis
Издательство: CRC Press
Год: 2023
Страниц: 522
Язык: английский
Формат: pdf (true)
Размер: 11.4 MB
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. 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.