Taxation and government spending are crucial tools for governments to promote economic growth and maintain social equity. However, the difficulty in accurately predicting the dynamic strategies of diverse self-interested households presents a challenge for governments to implement effective tax policies. Given its proficiency in modeling other agents in partially observable environments and adaptively learning to find optimal policies, Multi-Agent Reinforcement Learning (MARL) is highly suitable for solving dynamic games between the government and numerous households. Although MARL shows more potential than traditional methods such as the genetic algorithm and dynamic programming, there is a lack of large-scale multi-agent reinforcement learning economic simulators. Therefore, we propose a MARL environment, named \textbf{TaxAI}, for dynamic games involving $N$ households, government, firms, and financial intermediaries based on the Bewley-Aiyagari economic model. Our study benchmarks 2 traditional economic methods with 7 MARL methods on TaxAI, demonstrating the effectiveness and superiority of MARL algorithms. Moreover, TaxAI's scalability in simulating dynamic interactions between the government and 10,000 households, coupled with real-data calibration, grants it a substantial improvement in scale and reality over existing simulators. Therefore, TaxAI is the most realistic economic simulator, which aims to generate feasible recommendations for governments and individuals.

翻译：税收与政府支出是政府推动经济增长和维护社会公平的重要工具。然而，由于难以准确预测异质性自利家庭主体的动态策略，政府实施有效税收政策面临巨大挑战。多智能体强化学习（MARL）在部分可观测环境中对多主体建模及自适应学习最优策略方面具有显著优势，特别适用于解决政府与大量家庭之间的动态博弈问题。尽管MARL比遗传算法、动态规划等传统方法展现出更大潜力，但目前仍缺乏大规模多智能体强化学习经济模拟器。为此，本文基于Bewley-Aiyagari经济模型，提出了名为\textbf{TaxAI}的MARL环境，用于模拟包含N个家庭、政府、企业和金融中介的动态博弈。我们在TaxAI上对2种传统经济方法与7种MARL方法进行了基准测试，验证了MARL算法的有效性与优越性。此外，TaxAI可扩展至模拟政府与10,000个家庭之间的动态交互，并通过真实数据校准，在规模与真实性上显著优于现有模拟器。因此，TaxAI是最贴近现实的经济模拟器，旨在为政府和个人提供切实可行的政策建议。