Deep Reinforcement Learning in a Monetary Model

Understand

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.