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 for optimal tax policy, which aims to generate feasible recommendations for governments and individuals.

翻译：税收与政府支出是政府促进经济增长和维护社会公平的重要工具。然而，准确预测众多自利家庭主体的动态策略存在困难，这给政府实施有效税收政策带来了挑战。鉴于多智能体强化学习（MARL）在部分可观测环境中对其他智能体建模及自适应学习最优策略方面的能力，其非常适合解决政府与大量家庭之间的动态博弈问题。尽管MARL相比遗传算法和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的多智能体强化学习经济模拟器。为此，我们基于Bewley-Aiyagari经济模型，提出了一个名为\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。本研究在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算法的有效性和优越性。此外，TaxAI具备模拟政府与10,000个家庭动态交互的可扩展性，并结合真实数据校准，使其在规模和真实性上较现有模拟器有显著提升。因此，TaxAI是最接近真实经济系统的最优税收政策模拟器，旨在为政府和个人提供可行的决策建议。