Optimizing Priority-Based Search for Lifelong Multi-Agent Path Finding
Name
huang-huangn-meng-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
1.75 MB
Format
Adobe PDF
Checksum (MD5)
00264156d8b531c54caed3b804fbfa62
Author(s)
Huang, Natalie
Advisor(s)
Wu, Cathy
Date Issued
September 2025
Publisher
Massachusetts Institute of Technology
Abstract
The lifelong Multi-Agent Path Finding (MAPF) problem requires planning collision-free trajectories for agents operating continuously in dynamic environments. Traditional solvers such as Priority-Based Search (PBS) use fixed branching heuristics, which can be inefficient in high-congestion scenarios. This work explores how learning-based methods can improve PBS decision-making. We develop supervised learning (SL) policies trained from high-quality beam search trajectories and reinforcement learning (RL) policies learned directly through simulation, enabling adaptive branching strategies. Evaluations on warehouse-style and Kiva-style maps with varying agent densities show that learned policies can significantly boost throughput in congested warehouse layouts, while identifying scenarios where classical heuristics remain competitive. Our findings provide guidance on solver selection based on environment layout and congestion characteristics.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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In Copyright - Educational Use Permitted
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