LLM-Guided Reinforcement Learning for Interactive Environments
We propose herein <b>LLM-Guided Reinforcement Learning (LGRL)</b>, 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-leve...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/12/1932 |
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| Summary: | We propose herein <b>LLM-Guided Reinforcement Learning (LGRL)</b>, 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 <b>LGRL</b> achieves higher success rates, improved efficiency, and faster convergence compared to baseline approaches. |
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| ISSN: | 2227-7390 |