Recently, large language models (LLMs) have demonstrated remarkable potential as an intelligent agent. However, existing researches mainly focus on enhancing the agent's reasoning or decision-making abilities through well-designed prompt engineering or task-specific fine-tuning, ignoring the procedure of exploration and exploitation. When addressing complex tasks within open-world interactive environments, these methods exhibit limitations. Firstly, the lack of global information of environments leads to greedy decisions, resulting in sub-optimal solutions. On the other hand, irrelevant information acquired from the environment not only adversely introduces noise, but also incurs additional cost. This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE), to enhance LLM agents in solving open-world interactive tasks. Concretely, WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task. Our approach is flexible enough to incorporate diverse tasks, and obtains significant improvements in both success rates and efficiency across four interactive benchmarks.

翻译：最近，大语言模型（LLMs）在智能体领域展现出显著潜力。然而，现有研究主要通过精心设计的提示工程或任务特定微调来增强智能体的推理或决策能力，忽视了探索与利用的过程。在开放世界交互环境中处理复杂任务时，这些方法存在局限性。首先，缺乏环境全局信息会导致贪婪决策，产生次优解。另一方面，从环境中获取的无关信息不仅会引入噪声，还会带来额外成本。本文提出一种新颖方法——弱探索到强利用（WESE），以增强LLM智能体解决开放世界交互任务的能力。具体而言，WESE将探索与利用过程解耦，采用低成本的弱智能体执行探索任务以获取全局知识。随后引入基于知识图谱的策略来存储所获知识并提取任务相关知识，从而提升强智能体在利用任务中的成功率和效率。该方法具有足够的灵活性以整合不同任务，并在四个交互基准测试中显著提升了成功率和效率。