用强化学习求解异质主体宏观经济模型，效率高且能处理复杂市场出清条件。
Structural Reinforcement Learning for Heterogeneous Agent Macroeconomics
- 以价格为状态变量，用结构化强化学习直接学习均衡价格动态
- 在几分钟内全局求解克鲁塞尔-史密斯等三类经典模型
- 适合研究复杂宏观经济均衡的学者和量化经济学家
我们提出一种新的异质主体模型建模与求解方法，将横截面分布替换为低维价格作为状态变量，并让个体从模拟路径中直接学习均衡价格动态。为此，我们引入结构化强化学习（SRL）方法，通过模拟处理价格，同时利用个体对自身动态的结构性知识。该方法提供了一种通用且高效的全局求解方案，避免了主方程，可解决传统方法难以处理的问题，尤其是非平凡的市场出清条件。我们在克鲁塞尔-史密斯模型、带集体冲击的赫格特模型以及带有前瞻菲利普斯曲线的HANK模型中验证该方法，均在数分钟内完成全局求解。
原文摘要 · Abstract (English)
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.
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