Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/245436 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 9255
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
This exercise offers an innovative learning mechanism to model economic agent's decision-making process using a deep reinforcement learning algorithm. In particular, this AI agent is born in an economic environment with no information on the underlying economic structure and its own preference. I model how the AI agent learns from square one in terms of how it collects and processes information. It is able to learn in real time through constantly interacting with the environment and adjusting its actions accordingly (i.e., online learning). I illustrate that the economic agent under deep reinforcement learning is adaptive to changes in a given environment in real time. AI agents differ in their ways of collecting and processing information, and this leads to different learning behaviours and welfare distinctions. The chosen economic structure can be generalised to other decision-making processes and economic models.
Subjects: 
expectation formation
exploration
deep reinforcement learning
bounded rationality
stochastic optimal growth
JEL: 
C45
D83
D84
E21
E70
Document Type: 
Working Paper
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