JaxMARL-BC：秒级训练的多智能体宏观经济学模拟器
原标题：JaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling, in Seconds
AI 摘要
研究团队提出 JaxMARL-BC，一个基于 JAX 加速的多智能体强化学习宏观经济模拟器，将 MARL-BC 经济模型的训练时间从数小时缩短到数秒，并支持扩展到数十万个独立学习家庭。该工作验证了模型在已知 DSGE 极限下的正确性，给出简单的成本缩放规律，并用两小时完成此前需一个多月的异质资本回报模型校准实验。作者以开源形式发布该模拟器，旨在降低宏观经济学界采用 MARL 方法的计算门槛。
正文节选
JaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling, in Seconds Abstract. Multi-agent reinforcement learning (MARL) offers a principled way of populating simulated economies with many heterogeneous decision makers. Its adoption, pioneered in finance, has nonetheless been slow in macroeconomics because of two main limitations: the lack of a shared frame of reference connecting MARL systems to dynamic stochastic general equilibrium (DSGE) models the profession already trust