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

Tohid Atashbar and Rui Aruhan Shi

No 2023/040, IMF Working Papers from International Monetary Fund

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.

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 (search for similar items in EconPapers)
Pages: 31
Date: 2023-02-24
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dge
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Citations: View citations in EconPapers (4)

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