标题： LAMARL：基于大型语言模型辅助的多智能体强化学习的合作策略生成 显示英文标题

标题： LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation

摘要： 尽管多智能体强化学习（MARL）在复杂的多机器人任务中表现出色，但它存在样本效率低的问题，并且需要迭代的手动奖励调整。大型语言模型（LLMs）在单机器人设置中显示出潜力，但其在多机器人系统中的应用仍基本未被探索。本文介绍了一种新颖的LLM辅助MARL（LAMARL）方法，该方法将MARL与LLMs相结合，显著提高了样本效率，而无需人工设计。 LAMARL由两个模块组成：第一个模块利用LLMs完全自动化地生成先验策略和奖励函数。第二个模块是MARL，它使用生成的函数来有效指导机器人策略训练。 在一个形状组装基准测试中，模拟和真实世界实验均展示了LAMARL的独特优势。消融研究显示，先验策略平均提升了185.9%的样本效率并提高了任务完成度，而基于链式思维（CoT）和基本API的结构化提示将LLM输出成功率提高了28.5%-67.5%。 视频和代码可在https://windylab.github.io/LAMARL/获取。 显示英文摘要

摘要： Although Multi-Agent Reinforcement Learning (MARL) is effective for complex multi-robot tasks, it suffers from low sample efficiency and requires iterative manual reward tuning. Large Language Models (LLMs) have shown promise in single-robot settings, but their application in multi-robot systems remains largely unexplored. This paper introduces a novel LLM-Aided MARL (LAMARL) approach, which integrates MARL with LLMs, significantly enhancing sample efficiency without requiring manual design. LAMARL consists of two modules: the first module leverages LLMs to fully automate the generation of prior policy and reward functions. The second module is MARL, which uses the generated functions to guide robot policy training effectively. On a shape assembly benchmark, both simulation and real-world experiments demonstrate the unique advantages of LAMARL. Ablation studies show that the prior policy improves sample efficiency by an average of 185.9% and enhances task completion, while structured prompts based on Chain-of-Thought (CoT) and basic APIs improve LLM output success rates by 28.5%-67.5%. Videos and code are available at https://windylab.github.io/LAMARL/