Deep Reinforcement Learning in a Monetary Model

Mingli Chen, Andreas Joseph, Michael Kumhof +2

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 th…