AI and Macroeconomic Modeling: Deep Reinforcement Learning in an RBC model

Author/Editor:

Tohid Atashbar ; Rui Aruhan Shi

Publication Date:

February 24, 2023

Electronic Access:

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Summary:

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, one of which is deterministic without the technological shock and the other is stochastic. The objective of the deterministic environment is to compare the learning agent's behavior to a deterministic steady-state scenario. We demonstrate that in both deterministic and stochastic scenarios, the agent's choices are close to their optimal value. We also present cases of unstable learning behaviours. This AI-macro model may be enhanced in future research by adding additional variables or sectors to the model or by incorporating different DRL algorithms.

Series:

Working Paper No. 2023/040

Frequency:

regular

English

Publication Date:

February 24, 2023

ISBN/ISSN:

9798400235252/1018-5941

Stock No:

WPIEA2023040

Pages:

31

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