  In economic modeling, there has been an increasing investigation into
multi-agent simulators. Nevertheless, state-of-the-art studies establish the
model based on reinforcement learning (RL) exclusively for specific agent
categories, e.g., households, firms, or the government. It lacks concerns over
the resulting adaptation of other pivotal agents, thereby disregarding the
complex interactions within a real-world economic system. Furthermore, we pay
attention to the vital role of the government policy in distributing tax
credits. Instead of uniform distribution considered in state-of-the-art, it
requires a well-designed strategy to reduce disparities among households and
improve social welfare. To address these limitations, we propose an expansive
multi-agent economic model comprising reinforcement learning agents of numerous
types. Additionally, our research comprehensively explores the impact of tax
credit allocation on household behavior and captures the spectrum of spending
patterns that can be observed across diverse households. Further, we propose an
innovative government policy to distribute tax credits, strategically
leveraging insights from tax credit spending patterns. Simulation results
illustrate the efficacy of the proposed government strategy in ameliorating
inequalities across households.
