- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
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 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 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. This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners.
Features
Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithms
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 data scientists
Blends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding
To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book
Contents
Section 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.
