CognitiveAISystems/MAPF-GPT
[AAAI-2025] This repository contains MAPF-GPT, a deep learning-based model for solving MAPF problems. Trained with imitation learning on trajectories produced by LaCAM, it generates actions under partial observability without heuristics or agent communication. MAPF-GPT excels on unseen instances and outperforms learnable state-of-the-art solvers
This project helps operations managers, logistics planners, and robotics engineers efficiently coordinate many autonomous agents or robots within a shared space. It takes in a map of an environment and the desired starting and ending points for each agent, then outputs a set of synchronized actions, ensuring all agents reach their destinations without collisions or deadlocks. It's designed for anyone managing complex multi-agent systems where optimal movement is critical.
119 stars.
Use this if you need to find collision-free paths for a large number of agents (e.g., robots in a warehouse, drones in a delivery system) in complex, potentially unknown environments without needing extensive communication between them.
Not ideal if you're dealing with only a few agents or if your environment is simple enough for basic pathfinding algorithms.
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119
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Language
C++
License
MIT
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Last pushed
Mar 05, 2026
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