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Introduction to Reinforcement Learning

In: Machine Learning in Finance

Author

Listed:
  • Matthew F. Dixon

    (Illinois Institute of Technology, Department of Applied Mathematics)

  • Igor Halperin

    (New York University, Tandon School of Engineering)

  • Paul Bilokon

    (Imperial College London, Department of Mathematics)

Abstract

This chapter introduces Markov Decision Processes and the classical methods of dynamic programming, before building familiarity with the ideas of reinforcement learning and other approximate methods for solving MDPs. After describing Bellman optimality and iterative value and policy updates before moving to Q-learning, the chapter quickly advances towards a more engineering style exposition of the topic, covering key computational concepts such as greediness, batch learning, and Q-learning. Through a number of mini-case studies, the chapter provides insight into how RL is applied to optimization problems in asset management and trading.

Suggested Citation

  • Matthew F. Dixon & Igor Halperin & Paul Bilokon, 2020. "Introduction to Reinforcement Learning," Springer Books, in: Machine Learning in Finance, chapter 0, pages 279-345, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41068-1_9
    DOI: 10.1007/978-3-030-41068-1_9
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