Ashwin Rao, Tikhon Jelvis

Foundations of Reinforcement Learning with Applications in Finance (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book demystifies Reinforcement Learning, and makes it a practically useful tool for those studying and working in applied areas, especially finance. This book seeks to overcome that barrier, and to introduce the foundations of RL in a way that balances depth of understanding with clear, minimally technical delivery.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 522
• Erscheinungstermin: 16. Dezember 2022
• Englisch
• ISBN-13: 9781000801057
• Artikelnr.: 66213523

Autorenporträt

Ashwin Rao is the Chief Science Officer of Wayfair, an e-commerce company where he and his team develop mathematical models and algorithms for supply-chain and logistics, merchandising, marketing, search, personalization, pricing and customer service. Ashwin is also an Adjunct Professor at Stanford University, focusing his research and teaching in the area of Stochastic Control, particularly Reinforcement Learning algorithms with applications in Finance and Retail. Previously, Ashwin was a Managing Director at Morgan Stanley and a Trading Strategist at Goldman Sachs. Ashwin holds a Bachelor's degree in Computer Science and Engineering from IIT-Bombay and a Ph.D in Computer Science from University of Southern California, where he specialized in Algorithms Theory and Abstract Algebra.

Tikhon Jelvis is a programmer who specializes in bringing ideas from programming languages and functional programming to machine learning and data science. He has developed inventory optimization, simulation and demand forecasting systems as a Principal Scientist at Target and is a speaker and open-source contributor in the Haskell community where he serves on the board of directors for Haskell.org.

Inhaltsangabe

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.