  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}.
