Cover IMF Working Papers
AI and Macroeconomic Modeling: Deep Reinforcement Learning in an RBC model
Author:
Tohid Atashbar
Search for other papers by Tohid Atashbar in
Current site
Google Scholar
PubMedClose
and
Rui Aruhan Shi
Search for other papers by Rui Aruhan Shi in
Current site
Google Scholar
PubMedClose
Series:
IMF Working Papers
Volume/Issue:
Volume 2023: Issue 040
Language:
English
Publication Date:
24 Feb 2023
DOI:
https://doi.org/10.5089/9798400235252.001
ISBN:
9798400235252
ISSN:
1018-5941
Pages:
31
Keywords:
Reinforcement learning; Deep reinforcement learning; Artificial intelligence; RL; DRL; Learning algorithms; Macro modeling; RBC; Real business cycles; DDPG; Deep deterministic policy gradient; Actor-critic algorithms; RBC model; AI-macroeconomic simulator; AI experiment; learning algorithm; Machine learning; Rational expectations; Debt relief; Global
Legal-disclaimer:
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Download PDF (2.5 MB) Download Epub
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.
  • Collapse
  • Expand
Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund
Volume 2023: Issue 040