LLM-Guided Reinforcement Learning for Interactive Environments

We propose herein LLM-Guided Reinforcement Learning (LGRL), a novel framework that leverages large language models (LLMs) to decompose high-level objectives into a sequence of manageable subgoals in interactive environments. Our approach decouples high-level planning from low-level action execution by dynamically generating context-aware subgoals that guide the reinforcement learning (RL) agent. During training, intermediate subgoals—each associated with partial rewards—are generated based on the agent’s current progress, providing fine-grained feedback that facilitates structured exploration and accelerates convergence. At inference, a chain-of-thought strategy is employed, enabling the LLM to adaptively update subgoals in response to evolving environmental states. Although demonstrated on a representative interactive setting, our method is generalizable to a wide range of complex, goal-oriented tasks. Experimental results show that LGRL achieves higher success rates, improved efficiency, and faster convergence compared to baseline approaches.