Multi-agent collaboration with Large Language Models (LLMs) demonstrates
proficiency in basic tasks, yet its efficiency in more complex scenarios
remains unexplored. In gaming environments, these agents often face situations
without established coordination protocols, requiring them to make intelligent
inferences about teammates from limited data. This problem motivates the area
of ad hoc teamwork, in which an agent may potentially cooperate with a variety
of teammates to achieve a shared goal. Our study focuses on the ad hoc teamwork
problem where the agent operates in an environment driven by natural language.
Our findings reveal the potential of LLM agents in team collaboration,
highlighting issues related to hallucinations in communication. To address this
issue, we develop CodeAct, a general agent that equips LLM with enhanced memory
and code-driven reasoning, enabling the repurposing of partial information for
rapid adaptation to new teammates.

我们的研究关注于自然语言驱动的环境中的多代理协作问题，通过对 LLM 代理的研究，我们发现了 LLM 代理在团队协作中的潜力，并指出了与交流中的幻觉相关的问题。为了解决这个问题，我们开发了 CodeAct，一种具备增强记忆和代码驱动推理功能的通用代理，使其能够再利用部分信息以快速适应新团队成员。