用大模型提升经济政策模拟中的决策效率。
Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations
- 用预训练大语言模型替代强化学习，实现高效政策制定。
- 在三个环境中均显著优于现有方法，样本效率更高。
- 适合研究智能政策、多智能体系统与经济模拟的学者。
经济政策制定的改进可带来广泛的社会效益，这激发了对人工智能驱动政策工具的研究。AI政策制定有望通过大规模快速处理数据超越人类表现。然而，现有的基于强化学习的方法存在样本效率低的问题，且难以灵活融入复杂的细微信息。为此，我们提出一种新方法：在社会复杂的多智能体强化学习（MARL）场景中，利用预训练的大语言模型（LLMs）作为样本高效的政策制定者。我们在三个环境上验证了显著的效率提升，性能超越现有方法。代码已开源：https://github.com/hegasz/large-legislative-models。
原文摘要 · Abstract (English)
The improvement of economic policymaking presents an opportunity for broad societal benefit, a notion that has inspired research towards AI-driven policymaking tools. AI policymaking holds the potential to surpass human performance through the ability to process data quickly at scale. However, existing RL-based methods exhibit sample inefficiency, and are further limited by an inability to flexibly incorporate nuanced information into their decision-making processes. Thus, we propose a novel method in which we instead utilize pre-trained Large Language Models (LLMs), as sample-efficient policymakers in socially complex multi-agent reinforcement learning (MARL) scenarios. We demonstrate significant efficiency gains, outperforming existing methods across three environments. Our code is available at https://github.com/hegasz/large-legislative-models.
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