用大模型零样本解决多智能体调度，数据效率提升50倍
Data-Efficient Multi-Agent Spatial Planning with LLMs
- 通过提示工程激活大模型世界知识，实现零样本决策
- 仅需现有方法1/50的交互次数即可超越基准模型
- 适合需要低数据消耗的实时调度系统开发者
本项目旨在探索如何利用预训练大语言模型的世界知识，在多智能体决策中实现高效且鲁棒的学习。研究以出租车调度与分配问题为例，智能体需决定如何最优接载乘客以最小化整体等待时间。尽管该问题基于图结构道路网络，但通过恰当提示，大模型在零样本情况下表现强劲。此外，结合少量微调与逐次回溯算法进行前瞻规划，大模型仅需50倍少的环境交互次数即能超越现有方法。我们还考察了不同语言提示策略的效果，发现将某些易计算信息纳入提示可显著提升性能。最后，展示了大模型内置语义理解能力，可通过简单提示适应环境变化。
原文摘要 · Abstract (English)
In this project, our goal is to determine how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making. We examine this in a taxi routing and assignment problem where agents must decide how to best pick up passengers in order to minimize overall waiting time. While this problem is situated on a graphical road network, we show that with the proper prompting zero-shot performance is quite strong on this task. Furthermore, with limited fine-tuning along with the one-at-a-time rollout algorithm for look ahead, LLMs can out-compete existing approaches with 50 times fewer environmental interactions. We also explore the benefits of various linguistic prompting approaches and show that including certain easy-to-compute information in the prompt significantly improves performance. Finally, we highlight the LLM's built-in semantic understanding, showing its ability to adapt to environmental factors through simple prompts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。