Deep Reinforcement Learning in a Monetary Model
(深度强化学习在货币模型中的应用)
主讲人：Mingli Chen（University of Warwick, UK）
主持老师：（北大经院）王法
参与老师：（北大经院）王一鸣、王熙、刘蕴霆
时间：2025年3月26日（周三） 10:00-11:30
地点（线下）：北京大学经济学院107会议室
报告摘要：
We propose using deep reinforcement learning to solve dynamic stochastic general equilibrium models. Agents are represented by deep artificial neural networks and learn to solve their dynamic optimisation problem by interacting with the model environment, of which they have no a priori knowledge. Deep reinforcement learning offers a flexible yet principled way to model bounded rationality within this general class of models. We apply our proposed approach to a classical model from the adaptive learning literature in macroeconomics which looks at the interaction of monetary and fiscal policy. We find that, contrary to adaptive learning, the artificially intelligent household can solve the model in all policy regimes.
主讲人简介：
Mingli Chen is an Associate Professor in the Department of Economics at the University of Warwick, a Research Associate in CeMMAP, and a Turing Fellow at the Alan Turing Institute. Her research interests include Econometrics, Machine Learning, and AI in Economics. Her papers have been published in leading journals such as the Journal of Econometrics, the Journal of the Royal Statistical Society: Series B, and the Annals of Statistics. Starting in January 2024, she serves as an Associate Editor of the Journal of Econometrics.
