Deep Reinforcement Learning in a Monetary Model

Mingli Chen, Andreas Joseph, Michael Kumhof, Xinlei Pan and Xuan Zhou

Abstract: We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models. Agents are represented by deep artificial neural networks and learn to solve their dynamic optimisation problem by interacting with the model environment, of which they have no a priori knowledge. Deep reinforcement learning offers a flexible yet principled way to model bounded rationality within this general class of models. We apply our proposed approach to a classical model from the adaptive learning literature in macroeconomics which looks at the interaction of monetary and fiscal policy. We find that, contrary to adaptive learning, the artificially intelligent household can solve the model in all policy regimes.

Date: 2021-04, Revised 2023-01
New Economics Papers: this item is included in nep-big, nep-cba, nep-cmp, nep-dge and nep-mon
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2104.09368

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