Reinforcement Learning in Economics and Finance

Arthur Charpentier (), Romuald Élie () and Carl Remlinger
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Arthur Charpentier: Université du Québec à Montréal (UQAM)
Romuald Élie: LAMA, Université Gustave Eiffel, CNRS
Carl Remlinger: LAMA, Université Gustave Eiffel, CNRS

Computational Economics, 2023, vol. 62, issue 1, No 16, 425-462

Abstract: Abstract Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal rewards. As in online learning, the agent learns sequentially. As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices. In reinforcement learning, his actions have consequences: they influence not only rewards, but also future states of the world. The goal of reinforcement learning is to find an optimal policy – a mapping from the states of the world to the set of actions, in order to maximize cumulative reward, which is a long term strategy. Exploring might be sub-optimal on a short-term horizon but could lead to optimal long-term ones. Many problems of optimal control, popular in economics for more than forty years, can be expressed in the reinforcement learning framework, and recent advances in computational science, provided in particular by deep learning algorithms, can be used by economists in order to solve complex behavioral problems. In this article, we propose a state-of-the-art of reinforcement learning techniques, and present applications in economics, game theory, operation research and finance.

Keywords: Causality; Control; Machine learning; Markov decision process; Multi-armed bandits; Online-learning; Q-learning; Regret; Reinforcement learning; Rewards; Sequential learning (search for similar items in EconPapers)
JEL-codes: C18 C41 C44 C54 C57 C61 C63 C68 C70 C90 D40 D70 D83 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-021-10119-4

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