Language Guided Exploration for RL Agents in Text Environments
<|reference_start|>Language Guided Exploration for RL Agents in Text Environments: 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.<|reference_end|>
@article{golchha2024language,
    title={Language Guided Exploration for RL Agents in Text Environments},
    author={Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan, Keerthiram
  Murugesan},
    journal={arXiv preprint arXiv:2403.03141},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03141},
    primaryClass={cs.CL}
}