Recent studies have shown that Large Language Models (LLMs) can be utilized
for solving complex sequential decision-making tasks by providing high-level
instructions. However, LLM-based agents face limitations in real-time dynamic
environments due to their lack of specialization in solving specific target
problems. Moreover, the deployment of such LLM-based agents is both costly and
time-consuming in practical scenarios. In this paper, we introduce a novel
framework that addresses these challenges by training a smaller scale
specialized student agent using instructions from an LLM-based teacher agent.
By leveraging guided actions provided by the teachers, the prior knowledge of
the LLM is distilled into the local student model. Consequently, the student
agent can be trained with significantly less data. Furthermore, subsequent
training with environment feedback empowers the student agents to surpass the
capabilities of their teachers. We conducted experiments on three challenging
MiniGrid environments to evaluate the effectiveness of our framework. The
results demonstrate that our approach enhances sample efficiency and achieves
superior performance compared to baseline methods.

通过使用大规模语言模型，我们提出了一种新的框架，通过从语言模型教师代理接收指导行为，训练一个小规模专用的学生代理。通过将语言模型的先验知识融入到本地学生模型中，该学生代理可以用较少的数据进行训练，并通过环境反馈进一步提升其能力。实验结果表明，我们的方法提高了样本效率，并实现了比基准方法更卓越的性能。