Language Guided Exploration for RL Agents in Text Environments

• URL: http://arxiv.org/abs/2403.03141v1
• Date: Tue, 5 Mar 2024 17:26:41 GMT
• Title: Language Guided Exploration for RL Agents in Text Environments
• Authors: Hitesh Golchha, Sahil Yerawar, Dhruvesh Patel, Soham Dan, Keerthiram Murugesan
• Abstract summary: Large Language Models (LLMs), with a wealth of world knowledge, can help RL agents learn quickly and adapt to distribution shifts. We introduce Language Guided Exploration (LGE) framework, which uses a pre-trained language model to provide decision-level guidance to an RL agent (called EXPLORER) We observe that on ScienceWorld, a challenging text environment, LGE outperforms vanilla RL agents significantly and also outperforms other sophisticated methods like Behaviour Cloning and Text Decision Transformer.
• Score: 15.256908785183617
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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