  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.
