LLM-Empowered State Representation for Reinforcement Learning

Understand

Conventional state representations in reinforcement learning often omit critical task-related details, presenting a significant challenge for value networks in establishing accurate mappings from states to task rewards.

• Traditional methods typically depend on extensive sample learning to enrich state representations with task-specific information, which leads to low sample efficiency and high time costs.
• Recently, surging knowledgeable large language models (LLM) have provided promising substitutes for prior injection with minimal human intervention.
• Motivated by this, we propose LLM-Empowered State Representation (LESR), a novel approach that utilizes LLM to autonomously generate task-related state representation codes which help to enhance the continuity of network mappings and facilitate efficient training.