AI Driven Macroeconomic Forecasting and Policy Optimization Using Deep Reinforcement Learning

B. Purna Chandra Rao

AI Driven Macroeconomic Forecasting and Policy Optimization Using Deep Reinforcement Learning

Author(s)	B. Purna Chandra Rao
Country	India
Abstract	Modern macroeconomic systems operate under dynamic structural changes, policy transmission delays, and nonlinear interactions between economic variables. Traditional econometric forecasting models such as ARIMA, VAR, and DSGE rely on assumptions of stationarity and predefined structural relationships, limiting their adaptability under regime shifts, financial shocks, and inflation volatility. As global economies experience increasing uncertainty driven by geopolitical instability, supply chain disruptions, and rapid monetary tightening cycles, there is a growing need for adaptive forecasting and policy optimization frameworks capable of continuous learning. This paper presents an integrated macroeconomic forecasting and policy control framework using Deep Reinforcement Learning. The proposed system models the economy as a dynamic environment in which a learning agent observes macroeconomic state variables including GDP growth, inflation rate, unemployment, and interest rates, and determines optimal policy actions such as monetary adjustments and fiscal interventions. Unlike static forecasting approaches, the proposed method combines predictive modeling with feedback driven policy optimization, enabling adaptive responses to structural breaks and economic shocks. Baseline forecasting models including ARIMA, Vector Autoregression, and LSTM networks are implemented for comparative evaluation. Experimental results across multiple economic scenarios including stable growth, inflation shocks, and simulated crisis conditions demonstrate that the reinforcement learning based framework reduces forecasting error, lowers inflation volatility, and improves policy stabilization time compared to conventional approaches. The findings indicate that integrating deep reinforcement learning with macroeconomic modeling enables adaptive, data driven economic governance and enhances long term system resilience.
Keywords	Macroeconomic Forecasting, Deep Reinforcement Learning, Monetary Policy Optimization, GDP Prediction, Inflation Modeling, Economic Stability, Adaptive Policy Systems, Computational Economics, Reinforcement Learning Control, AI for Public Policy.
Field	Engineering
Published In	Volume 7, Issue 4, April 2026
Published On	2026-04-27

View / Download PDF File

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 12 Cover Page Vol 6 Isu 10 Cover Page Vol 6 Isu 9

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJLRP Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijlrp.com

International Journal of Leading Research Publication

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

AI Driven Macroeconomic Forecasting and Policy Optimization Using Deep Reinforcement Learning

Share this