Multi-Agent Path Finding via Decision Transformer and LLM Collaboration

ICLR 2025 Conference Submission12697 Authors

Keywords: Multi-Agent Path Finding, Multi-Agent Reinforcement Learning, Decision Transformer, Large Language Models, Autonomous Agents

Abstract:

Multi-Agent Path Finding (MAPF) is a significant problem with pivotal applications in robotics and logistics. The problem involves determining collision-free paths for multiple agents with specific goals in a 2D grid-world environment. Unfortunately, finding optimal solutions for MAPF is an NP-hard problem. Traditional centralized planning approaches are intractable for large numbers of agents and inflexible when adapting to
dynamic changes in the environment. On the other hand, existing decentralized methods utilizing learning-based strategies suffer from two main drawbacks: (1) training takes times ranging from days to weeks, and (2) they often tend to exhibit self-centered agent behaviors leading to increased collisions. We introduce a novel approach leveraging the Decision Transformer (DT) architecture that enables agents to learn individual policies efficiently. We capitalize on the transformer's capability for long-horizon planning and the advantages of offline reinforcement learning to drastically reduce training times to a few hours. We further show that integrating an LLM (GPT-4o), enhances the performance of DT policies in mitigating undesirable behaviors such as prolonged idling at specific positions and undesired deviations from goal positions. We focus our empirical evaluation on both scenarios with static environments and in dynamically changing environments where agents' goals are altered during inference. Results demonstrate that incorporating an LLM for dynamic scenario adaptation in MAPF significantly enhances the agents' performance and paves the way for more adaptable multi-agent systems.

Primary Area: reinforcement learning

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Submission Number: 12697