无需训练即可提升多智能体路径规划效率，缓解拥堵问题。
STEAM: A Training-Free Congestion-Aware Enhancement Framework for Decentralized Multi-Agent Path Finding
- 通过实时分析路径预测拥堵点，动态调整代价信息
- 成功率达60%提升，平均完成时间与路径成本显著降低
- 适合部署在已训练好的分布式路径规划系统中
我们提出STEAM（空间、时间与涌现拥堵感知的MAPF框架），一种无需训练的测试时增强方法，用于离散环境中的基于学习的分布式多智能体路径规划（MAPF）。给定预训练的分布式策略，STEAM无需重新训练、修改架构或替换为集中式规划器，而是向原始策略执行中注入轻量级的拥堵感知引导。首先，根据当前代价到目标图滚动推出最短路径，识别潜在未来拥堵热点。对可规避的空间拥堵，通过更新个体代价到目标信息进行缓解；对不可规避的瓶颈，则通过时间维度的逻辑值修正处理。此外，基于邻近智能体修正后的代价到目标图，采用密度感知的逻辑值修正减少局部涌现拥堵。在代表性基于学习的分布式MAPF算法上进行大量实验表明，STEAM持续提升成功率、使工期和解的成本，成功率最高提升达60%，且计算开销极小。实现代码见https://anonymous.4open.science/r/STEAM-MAPF-7A62。
原文摘要 · Abstract (English)
We propose STEAM (Spatial, Temporal, and Emergent congestion Awareness for MAPF), a training-free test-time enhancement framework for learning-based decentralized Multi-Agent Path Finding (MAPF) in discrete environments. Given a pretrained decentralized policy, STEAM requires no retraining, architectural modification, or replacement by a centralized planner. Instead, it injects lightweight congestion-aware guidance into the original policy execution. STEAM first rolls out the shortest paths induced by the current cost-to-go maps to identify potential future congestion hotspots. Spatially avoidable congestion is mitigated by updating agent-specific cost-to-go information, while spatially unavoidable bottlenecks are handled through temporal logit correction. In addition, emergent local congestion is reduced by a density-aware logit correction based on neighboring agents' corrected cost-to-go maps. Extensive experiments on representative learning-based decentralized MAPF algorithms show that STEAM consistently improves success rate, makespan, and solution cost, with success-rate gains of up to 60% and only minor computational overhead. The implementation is available at https://anonymous.4open.science/r/STEAM-MAPF-7A62.
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。