IMF Working Papers

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

By Tohid Atashbar, Rui Aruhan Shi

February 24, 2023

Download PDF

Preview Citation

Format: Chicago

Tohid Atashbar, and Rui Aruhan Shi. AI and Macroeconomic Modeling: Deep Reinforcement Learning in an RBC model, (USA: International Monetary Fund, 2023) accessed November 21, 2024

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.

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.

Keywords: Actor-critic algorithms, Artificial intelligence, DDPG, Deep deterministic policy gradient, Deep reinforcement learning, DRL, Learning algorithms, Macro modeling, RBC, Real business cycles, Reinforcement learning, RL

Publication Details

  • Pages:

    31

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2023/040

  • Stock No:

    WPIEA2023040

  • ISBN:

    9798400235252

  • ISSN:

    1018-5941