Deep Reinforcement Learning in a Monetary Model
This addresses the problem of modeling bounded rationality in macroeconomic policy analysis for economists, but it is incremental as it applies an existing AI method to a classical model.
The authors tackled solving dynamic stochastic general equilibrium models by using deep reinforcement learning with neural networks, finding that the AI household could solve the model in all policy regimes, unlike adaptive learning methods.
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.