LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2509.00347v1
• Date: Sat, 30 Aug 2025 04:02:33 GMT
• Title: LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning
• Authors: Hanping Zhang, Yuhong Guo,
• Abstract summary: Reinforcement Learning (RL) is known for its strong decision-making capabilities and has been widely applied in various real-world scenarios.<n>Due to the limitations of offline data, RL agents often struggle to generalize to new tasks or environments.<n>We propose LLM-Driven Policy Diffusion (LLMDPD), a novel approach that enhances generalization in offline RL using task-specific prompts.
• Score: 23.628360655654507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement Learning (RL) is known for its strong decision-making capabilities and has been widely applied in various real-world scenarios. However, with the increasing availability of offline datasets and the lack of well-designed online environments from human experts, the challenge of generalization in offline RL has become more prominent. Due to the limitations of offline data, RL agents trained solely on collected experiences often struggle to generalize to new tasks or environments. To address this challenge, we propose LLM-Driven Policy Diffusion (LLMDPD), a novel approach that enhances generalization in offline RL using task-specific prompts. Our method incorporates both text-based task descriptions and trajectory prompts to guide policy learning. We leverage a large language model (LLM) to process text-based prompts, utilizing its natural language understanding and extensive knowledge base to provide rich task-relevant context. Simultaneously, we encode trajectory prompts using a transformer model, capturing structured behavioral patterns within the underlying transition dynamics. These prompts serve as conditional inputs to a context-aware policy-level diffusion model, enabling the RL agent to generalize effectively to unseen tasks. Our experimental results demonstrate that LLMDPD outperforms state-of-the-art offline RL methods on unseen tasks, highlighting its effectiveness in improving generalization and adaptability in diverse settings.

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