标题： EconGym：一个具有多种经济任务的可扩展AI试验台 显示英文标题

标题： EconGym: A Scalable AI Testbed with Diverse Economic Tasks

摘要： 人工智能（AI）已成为经济研究的强大工具，能够实现大规模仿真和政策优化。 然而，有效应用AI需要可扩展的训练和评估平台——但现有的环境仍然局限于简化且范围狭窄的任务，无法充分捕捉诸如人口结构变化、多政府协调以及大规模主体互动等复杂的经济挑战。 为解决这一差距，我们引入了EconGym，这是一个可扩展且模块化的测试平台，将各种经济任务与AI算法连接起来。 基于严格的经济建模，EconGym实现了11种异质角色类型 （例如家庭、企业、银行、政府），它们的交互机制以及具有明确观测、行动和奖励的主体模型。 用户可以灵活组合经济角色与多种主体算法，在超过25项经济任务中模拟丰富的多主体轨迹，用于AI驱动的政策学习和分析。 实验表明，EconGym支持多样化和跨领域的任务——例如协调财政、养老金和货币政策——并且能够在AI、经济方法以及混合方法之间进行基准测试。 结果显示，更丰富的任务组合和算法多样性扩大了政策空间，而由经典经济方法指导的AI代理在复杂环境中表现最佳。 EconGym还可以高效地扩展到10,000个主体，并保持高度的真实性和效率。 显示英文摘要

摘要： Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks-such as coordinating fiscal, pension, and monetary policies-and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings. EconGym also scales to 10k agents with high realism and efficiency.