Real-world sequential decision making is characterized by sparse rewards and
large decision spaces, posing significant difficulty for experiential learning
systems like $\textit{tabula rasa}$ reinforcement learning (RL) agents. Large
Language Models (LLMs), with a wealth of world knowledge, can help RL agents
learn quickly and adapt to distribution shifts. In this work, we introduce
Language Guided Exploration (LGE) framework, which uses a pre-trained language
model (called GUIDE ) to provide decision-level guidance to an RL agent (called
EXPLORER). We observe that on ScienceWorld (Wang et al.,2022), a challenging
text environment, LGE outperforms vanilla RL agents significantly and also
outperforms other sophisticated methods like Behaviour Cloning and Text
Decision Transformer.

通过使用预训练语言模型（称为 GUIDE）为强化学习代理（称为 EXPLORER）提供决策级别的指导，我们引入了 Language Guided Exploration（LGE）框架，并观察到 LGE 在具有挑战性的文本环境中显著优于传统强化学习代理、行为克隆和文本决策转换等先进方法。