{"ID":2826624,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18892","arxiv_id":"2512.18892","title":"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.","short_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...","url_abs":"https://arxiv.org/abs/2512.18892","url_pdf":"https://arxiv.org/pdf/2512.18892v1","authors":"[\"Yucheng Yang\",\"Chiyuan Wang\",\"Andreas Schaab\",\"Benjamin Moll\"]","published":"2025-12-21T21:22:12Z","proceeding":"econ.TH","tasks":"[\"econ.TH\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}