Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics
Abstract
We present a new approach to formulating and solving heterogeneous agent models with aggregate risk. We replace the cross-sectional distribution with low-dimensional prices as state variables and let agents learn equilibrium price dynamics directly from simulated paths. To do so, we introduce a structural reinforcement learning (SRL) method which treats prices via simulation while exploiting agents' structural knowledge of their own individual dynamics. Our SRL method yields a general and highly efficient global solution method for heterogeneous agent models that sidesteps the Master equation and handles problems traditional methods struggle with, in particular nontrivial market-clearing conditions. We illustrate the approach in the Krusell-Smith model, the Huggett model with aggregate shocks, and a HANK model with a forward-looking Phillips curve, all of which we solve globally within minutes.
Cite
@article{arxiv.2512.18892,
title = {Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics},
author = {Yucheng Yang and Chiyuan Wang and Andreas Schaab and Benjamin Moll},
journal= {arXiv preprint arXiv:2512.18892},
year = {2025}
}