Reinforcement Learning From Experience Feedback: Application To Economic Policy

This article discusses the use of Reinforcement Learning from Experience Feedback (RLXF) in economic policymaking. RLXF integrates historical experiences into large language models (LLMs) to generate more informed policy suggestions. A case study using the IMF’s MONA database demonstrates the potential of RLXF in equipping AI with a nuanced perspective. However, there are potential risks and limitations in relying heavily on historical data.