Reinforcement learning and stochastic optimisation

Sebastian Jaimungal ()
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Sebastian Jaimungal: University of Toronto

Finance and Stochastics, 2022, vol. 26, issue 1, No 6, 103-129

Abstract: Abstract At the heart of financial mathematics lie stochastic optimisation problems. Traditional approaches to solving such problems, while applicable to broad classes of models, require specifying a model to complete the analysis and obtain implementable results. Even then, the curse of dimensionality challenges the viability of conventional methods to settings of practical relevance. In contrast, machine learning, and reinforcement learning (RL) particularly, promises to learn from data and overcome the curse of dimensionality simultaneously. This article touches on several approaches in the extant literature that are well positioned to merge our traditional techniques with RL.

Keywords: Stochastic optimisation; Stochastic games; Reinforcement learning; Machine learning; 93E20; 93E35; 91G60; 91G80 (search for similar items in EconPapers)
JEL-codes: C02 G11 G12 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s00780-021-00467-2

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