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

Author

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  • Tohid Atashbar
  • Rui Aruhan Shi

Abstract

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.

Suggested Citation

  • Tohid Atashbar & Rui Aruhan Shi, 2023. "AI and Macroeconomic Modeling: Deep Reinforcement Learning in an RBC model," IMF Working Papers 2023/040, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2023/040
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    Cited by:

    1. Qirui Mi & Zhiyu Zhao & Siyu Xia & Yan Song & Jun Wang & Haifeng Zhang, 2024. "Learning Macroeconomic Policies based on Microfoundations: A Stackelberg Mean Field Game Approach," Papers 2403.12093, arXiv.org, revised Oct 2024.
    2. Kshama Dwarakanath & Jialin Dong & Svitlana Vyetrenko, 2024. "Tax Credits and Household Behavior: The Roles of Myopic Decision-Making and Liquidity in a Simulated Economy," Papers 2408.10391, arXiv.org, revised Oct 2024.
    3. Kshama Dwarakanath & Svitlana Vyetrenko & Tucker Balch, 2024. "Empirical Equilibria in Agent-based Economic systems with Learning agents," Papers 2408.12038, arXiv.org.

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