用JAX实现快速可扩展的经济模拟，15分钟完成百人智能体训练。
EconoJax: A Fast & Scalable Economic Simulation in Jax
- 基于JAX构建全框架经济模拟系统，支持大规模群体并行训练。
- 100个智能体在15分钟内生成真实经济行为，速度提升数十倍。
- 开源代码助力多智能体经济研究，适合强化学习与制度设计者。
准确的经济模拟通常需要大量实验运行，尤其当结合强化学习时更为明显。然而，在多智能体经济环境中训练强化学习代理往往耗时较长。本文提出EconoJax，一个基于人工智能经济学家的快速模拟经济系统，其全部代码基于JAX实现。该设计使EconoJax可扩展至大规模人群，并在数分钟内完成大型实验。在包含100个智能体的实验中，我们仅用15分钟便观察到真实经济行为的涌现，相较以往需数日的工作效率显著提升。此外，我们测试了不同动作空间规模下多种多智能体方法的行为多样性，结果表明这些方法间未产生显著差异，与早期研究中的假设相反。为促进后续研究，EconoJax已开源至GitHub。
原文摘要 · Abstract (English)
Accurate economic simulations often require many experimental runs, particularly when combined with reinforcement learning. Unfortunately, training reinforcement learning agents in multi-agent economic environments can be slow. This paper introduces EconoJax, a fast simulated economy, based on the AI economist. EconoJax, and its training pipeline, are completely written in JAX. This allows EconoJax to scale to large population sizes and perform large experiments, while keeping training times within minutes. Through experiments with populations of 100 agents, we show how real-world economic behavior emerges through training within 15 minutes, in contrast to previous work that required several days. We additionally perform experiments in varying sized action spaces to test if some multi-agent methods produce more diverse behavior compared to others. Here, our findings indicate no notable differences in produced behavior with different methods as is sometimes suggested in earlier works. To aid further research, we open-source EconoJax on Github.
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