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Chapter 21

Reinforcement Learning

Curated summaries of the key literature behind this chapter — the findings, the methods, and how to put them to work.

24 referenced in this chapter

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R2P students unlock curated summaries of every key paper behind this chapter — the core findings, the methods, and how to apply them — plus resources we keep adding over time.

Deep Reinforcement Learning for Trading—A Critical Survey
Adrian Millea (2021)
QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
Igor Halperin (2019)
Generative Adversarial Imitation Learning
Jonathan Ho and Stefano Ermon (2016)
Machine Learning for Market Microstructure and High Frequency Trading
Michael Kearns and Yuriy Nevmyvaka
Modern Perspectives on Reinforcement Learning in Finance
Petter N. Kolm and Gordon Ritter (2019)
plus 19 more references inside
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