We propose new global solution algorithms for continuous time heterogeneous agent economies with aggregate shocks. We first approximate the state space so the master equation becomes a high, but finite, dimensional partial differential equation. We then approximate the value function using neural networks and solve the master equation using deep learning tools. The main advantage of this technique is that it allows us to find global solutions to high dimensional, non-linear problems. We demonstrate our algorithms by solving two canonical models in the macroeconomics literature: the Aiyagari (1994) model and the Krusell and Smith (1998) model. Joint work with Zhouzhou Gu, Sebastian Merkel and Jonathan Payne.