Language Guided Exploration for RL Agents in Text Environments
Hitesh Golchha Sahil Yerawar Dhruvesh Patel Soham Dan Keerthiram Murugesan
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.