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New York University (NYU)

Overview of Advanced Methods of Reinforcement Learning in Finance

New York University (NYU) via Coursera


In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance. In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high-frequency trading, cryptocurrencies, peer-to-peer lending, and more. After taking this course, students will be able to - explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability, - discuss market modeling, - Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.


  • Black-Scholes-Merton model, Physics and Reinforcement Learning
  • Reinforcement Learning for Optimal Trading and Market Modeling
  • Perception - Beyond Reinforcement Learning
  • Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.

Taught by

Igor Halperin


1.0 rating, based on 1 Class Central review

3.9 rating at Coursera based on 79 ratings

Start your review of Overview of Advanced Methods of Reinforcement Learning in Finance

  • Cyril A Furtado
    Lecture material is good, but lectures are in a monotone voice behind a slide presentation, very few examples, voice intensity is low. Lecture should spend a little more time in holding the attention of the student.

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