The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents. However, the substantial cost on maintaining the prolonged agent interactions poses challenge over the deployment of believable LLM-based agents. Therefore, in this paper, we develop Affordable Generative Agents (AGA), a framework for enabling the generation of believable and low-cost interactions on both agent-environment and inter-agents levels. Specifically, for agent-environment interactions, we substitute repetitive LLM inferences with learned policies; while for inter-agent interactions, we model the social relationships between agents and compress auxiliary dialogue information. Extensive experiments on multiple environments show the effectiveness and efficiency of our proposed framework. Also, we delve into the mechanisms of emergent believable behaviors lying in LLM agents, demonstrating that agents can only generate finite behaviors in fixed environments, based upon which, we understand ways to facilitate emergent interaction behaviors. Our code is publicly available at: \url{https://github.com/AffordableGenerativeAgents/Affordable-Generative-Agents}.

翻译：大型语言模型（LLMs）的兴起显著推进了可信交互式智能体的模拟进程。然而，维持长期智能体交互的高昂成本对部署基于LLM的可信智能体构成了挑战。为此，本文提出了经济型生成式智能体（AGA）框架，该框架能够在智能体-环境交互与智能体间交互两个层面实现低成本的可靠交互生成。具体而言，针对智能体-环境交互，我们采用学习到的策略替代重复的LLM推理；针对智能体间交互，我们建模智能体间的社会关系并压缩辅助对话信息。在多个环境中的大量实验证明了我们所提框架的有效性和高效性。此外，我们深入探究了LLM智能体中涌现可信行为的机制，表明在固定环境中智能体仅能生成有限行为，并基于此理解促进涌现交互行为的方法。我们的代码已开源在：\url{https://github.com/AffordableGenerativeAgents/Affordable-Generative-Agents}。