Inverse Reinforcement Learning and Imitation Learning

Matthew F. Dixon, Igor Halperin and Paul Bilokon
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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

Chapter Chapter 11 in Machine Learning in Finance, 2020, pp 419-517 from Springer

Abstract: Abstract This chapter provides an overview of the most popular methods of inverse reinforcement learning (IRL) and imitation learning (IL). These methods solve the problem of optimal control in a data-driven way, similarly to reinforcement learning, however with the critical difference that now rewards are not observed. The problem is rather to learn the reward function from the observed behavior of an agent. As behavioral data without rewards is widely available, the problem of learning from such data is certainly very interesting. This chapter provides a moderate-level technical description of the most promising IRL methods, equips the reader with sufficient knowledge to understand and follow the current literature on IRL, and presents examples that use simple simulated environments to evaluate how these methods perform when the “ground-truth” rewards are known. We then present use cases for IRL in quantitative finance which include applications in trading strategy identification, sentiment-based trading, option pricing, inference of portfolio investors, and market modeling.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41068-1_11

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DOI: 10.1007/978-3-030-41068-1_11

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