Large Language Models (LLM) based agents have shown promise in autonomously
completing tasks across various domains, e.g., robotics, games, and web
navigation. However, these agents typically require elaborate design and expert
prompts to solve tasks in specific domains, which limits their adaptability. We
introduce AutoManual, a framework enabling LLM agents to autonomously build
their understanding through interaction and adapt to new environments.
AutoManual categorizes environmental knowledge into diverse rules and optimizes
them in an online fashion by two agents: 1) The Planner codes actionable plans
based on current rules for interacting with the environment. 2) The Builder
updates the rules through a well-structured rule system that facilitates online
rule management and essential detail retention. To mitigate hallucinations in
managing rules, we introduce \textit{case-conditioned prompting} strategy for
the Builder. Finally, the Formulator agent compiles these rules into a
comprehensive manual. The self-generated manual can not only improve the
adaptability but also guide the planning of smaller LLMs while being
human-readable. Given only one simple demonstration, AutoManual significantly
improves task success rates, achieving 97.4\% with GPT-4-turbo and 86.2\% with
GPT-3.5-turbo on ALFWorld benchmark tasks. The source code will be available
soon.

通过自动生成规则和提高适应性，AutoManual 框架使基于大型语言模型（LLM）的代理能够自主构建自身的理解并适应新的环境。在 ALFWorld 基准任务上，通过 GPT-4-turbo 和 GPT-3.5-turbo，AutoManual 显著提高了任务成功率，并生成了人可读的综合手册。