Deep reinforcement learning in a monetary model

Mingli Chen, Rama Cont, Andreas Joseph, Michael Kumhof, Xinlei Pan, Wei Xiong and Xuan Zhou
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Mingli Chen: University of Oxford
Rama Cont: University of Warwick
Andreas Joseph: Bank of England
Michael Kumhof: Bank of England
Xinlei Pan: University of California (Berkeley)
Wei Xiong: University of Oxford
Xuan Zhou: Reserve Bank of Australia

No 1142, Bank of England Staff Working Paper series from Bank of England

Abstract: We propose deep reinforcement learning (DRL) as a general approach to bounded rationality in dynamic stochastic general equilibrium (DSGE) models. Agents are represented by deep artificial neural networks and learn to maximise their intertemporal objective function by interacting with an a priori unknown environment. Applying this approach to a model from the adaptive learning literature, DRL agents can learn all equilibria irrespective of local stability properties. However, learning is slow and may be unstable without the imposition of early stopping criteria. These findings can have implications for the use and interpretation of DRL agents and of DSGE models more generally.

Keywords: Artificial intelligence; deep reinforcement learning; adaptive learning; monetary policy; fiscal policy; multiple equilibri (search for similar items in EconPapers)
JEL-codes: C14 C52 D83 E52 E62 (search for similar items in EconPapers)
Pages: 47
Date: 2025-09-26
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