Data-Efficient Multi-Agent Spatial Planning with LLMs

• URL: http://arxiv.org/abs/2502.18822v1
• Date: Wed, 26 Feb 2025 04:53:07 GMT
• Title: Data-Efficient Multi-Agent Spatial Planning with LLMs
• Authors: Huangyuan Su, Aaron Walsman, Daniel Garces, Sham Kakade, Stephanie Gil,
• Abstract summary: We show how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making.<n>We examine a taxi routing and assignment problem where agents must decide how to best pick up passengers in order to minimize overall waiting time.
• Score: 8.872100864022675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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