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• W3012922890 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." @default.
• W3012922890 created "2020-03-27" @default.
• W3012922890 creator A5029610031 @default.
• W3012922890 creator A5029853129 @default.
• W3012922890 creator A5085971810 @default.
• W3012922890 date "2020-03-22" @default.
• W3012922890 modified "2023-10-01" @default.
• W3012922890 title "Reinforcement Learning in Economics and Finance." @default.
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