Description
Book SynopsisFoundations 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 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 technically challenging.
This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implem
Trade Review
“This book is a nice addition to the literature on Reinforcement Learning (RL), offering comprehensive coverage of both foundational RL techniques and their applications in the field of finance. It has the potential to be a foundational reference for both practitioners and researchers in finance. The book delves into essential RL concepts such as Markov Decision Processes (MDPs), Dynamic Programming, Policy Optimization, Actor-Critic models, Multi-armed Bandits, and Regret Bounds.
Despite its finance-oriented approach, individuals without an extensive financial background but possessing a decent machine learning (ML) background will find it easy to read this book.
By encompassing all of the major asset classes including equities, fixed income and derivatives, the book caters to a broad range of readers, enabling them to apply RL techniques to diverse financial scenarios. In summary, this book is an outstanding resource that combines RL fundamentals with practical applications in finance.”
– Natesh Pillai, Department of Statistics, Harvard University, Unites States of America
Table of ContentsSection I. Processes and Planning Algorithms. 1. Markov Processes. 2. Markov Decision Processes. 3. Dynamic Programming Algorithms. 4. Function Approximation and Approximate Dynamic Programming. Section II. Modeling Financial Applications. 5. Utility Theory. 6. Dynamic Asset-Allocation and Consumption. 7. Derivatives Pricing and Hedging. 8. Order-Book Trading Algorithms. Section III. Reinforcement Learning Algorithms. 9. Monte-Carlo and Temporal-Difference for Prediction. 10. Monte-Carlo and Temporal-Difference for Control. 11. Batch RL, Experience-Replay, DQN, LSPI, Gradient TD. 12. Policy Gradient Algorithms. Section IV. Finishing Touches. 13. Multi-Armed Bandits: Exploration versus Exploitation. 14. Blending Learning and Planning. 15. Summary and Real-World Considerations. Appendices.
