01628nam a2200169   450000500170000000800410001702000180005804100080007608200170008410000200010124500870012126000290020830000150023752011680025265000270142065000110144720260311232850.0260310b        |||||||| |||| 00| 0 eng d  a9789355428516  aeng  a005.13bHilR  aHilpisch, Yves   aReinforcement Learning for Finance :bA Python-Based Introduction /cYves Hilpisch  aMumbai:bShroff,c©2025  axii, 198p.  aReinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This book is among the first to explore the use of reinforcement learning methods in finance. Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems.This book covers:Reinforcement learning Deep Q-learning Python implementations of these algorithms How to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocation This book is the ideal reference on this topic. You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance.  aFinancial Applications  aPython