让大模型规划、强化学习执行，提升复杂任务决策效率。
Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks
- 大模型生成目标与计划，强化学习优化具体动作。
- 在复杂序列任务中成功率更高，样本效率提升显著。
- 适合需要长期规划与精细控制的智能体开发。
大型语言模型（LLMs）在推理、规划和工具使用方面展现出强大能力，推动了新型自主智能体的发展。然而，基于LLM的智能体在需要精确动作优化与环境交互的长时序序列决策任务中表现不佳。强化学习（RL）虽在序列控制上有效，但缺乏复杂场景所需的高层抽象与任务分解能力。本文提出一种融合LLM规划与RL动作优化的混合智能体架构：由LLM生成子目标、结构化计划和上下文引导，由RL智能体通过环境交互精炼底层动作。在序列决策任务上的实验表明，该方法相比纯RL或纯LLM基线，实现了更高的成功率、更优的样本效率以及更连贯的动作轨迹。该混合范式为构建更强大的自主系统提供了新方向。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents. However, LLM-based agents struggle with long-horizon sequential decision tasks that require precise action optimization and environment interaction. Reinforcement Learning (RL), while effective for sequential control, often lacks the high-level abstraction and task decomposition abilities needed for complex scenarios. This paper introduces an LLM-Augmented Reinforcement Learning Agent that integrates LLM-driven planning with RL-based action optimization. The proposed architecture leverages the LLM to generate subgoals, structured plans, and contextual guidance, while the RL agent refines low-level actions through interaction with the environment. Experiments on sequential decision tasks demonstrate improved sample efficiency, higher success rates, and more coherent action trajectories compared to RL-only and LLM-only baselines. This hybrid paradigm highlights a promising direction for building more capable autonomous systems.
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