用深度多智能体强化学习模拟经济中异质主体，兼顾传统模型精度与灵活性。
Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning
- 结合强化学习与真实经济周期模型，让智能体自主学习行为策略。
- 单主体时复现经典RBC模型结果，多相同主体时匹配均值场KS模型。
- 可高效模拟复杂异质性，适合研究不完全理性经济行为的学者。
当前具有主体异质性的宏观经济学模型主要分为两类：基于一般均衡与理性预期的异质主体新凯恩斯（HANK）或克鲁塞尔-史密斯（KS）模型，虽理论严谨但计算复杂，难以刻画丰富异质性；而基于主体的模型（ABMs）虽能灵活建模大量异质个体，却需人工设定行为规则，开发耗时。为此，本文提出MARL-BC框架，将深度多智能体强化学习（MARL）与真实经济周期（RBC）模型融合。实验表明：（1）单智能体下可复现经典RBC结果；（2）大量同质智能体可重现均值场KS模型结果；（3）有效模拟复杂异质性，是传统一般均衡方法难以实现的。该框架在异质交互场景下表现为一种新型ABM，而在极限情况下可还原一般均衡结果，推动了两类建模范式的融合。
原文摘要 · Abstract (English)
Current macroeconomic models with agent heterogeneity can be broadly divided into two main groups. Heterogeneous-agent general equilibrium (GE) models, such as those based on Heterogeneous Agent New Keynesian (HANK) or Krusell-Smith (KS) approaches, rely on GE and 'rational expectations', somewhat unrealistic assumptions that make the models very computationally cumbersome, which in turn limits the amount of heterogeneity that can be modelled. In contrast, agent-based models (ABMs) can flexibly encompass a large number of arbitrarily heterogeneous agents, but typically require the specification of explicit behavioural rules, which can lead to a lengthy trial-and-error model-development process. To address these limitations, we introduce MARL-BC, a framework that integrates deep multi-agent reinforcement learning (MARL) with real business cycle (RBC) models. We demonstrate that MARL-BC can: (1) recover textbook RBC results when using a single agent; (2) recover the results of the mean-field KS model using a large number of identical agents; and (3) effectively simulate rich heterogeneity among agents, a hard task for traditional GE approaches. Our framework can be thought of as an ABM if used with a variety of heterogeneous interacting agents, and can reproduce GE results in limit cases. As such, it is a step towards a synthesis of these often opposed modelling paradigms.
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