MAPF-GPT and MAPF-GPT-DDG
MAPF-GPT-DDG is a specialized extension of MAPF-GPT that adds decentralized multi-agent capability and Delta D fine-tuning, making them complementary tools where the latter builds upon the former's imitation learning foundation rather than competing alternatives.
About MAPF-GPT
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.
About MAPF-GPT-DDG
Cognitive-AI-Systems/MAPF-GPT-DDG
[IROS-2025] MAPF-GPT-DDG is a scalable decentralized multi-agent pathfinding (MAPF) solver based on imitation learning. It builds upon MAPF-GPT by introducing a novel fine-tuning method called Delta Data Generation (DDG) — a reward-free active learning approach that identifies and corrects failure cases in the policy.
This project helps robotics engineers and logistics planners efficiently coordinate multiple autonomous agents, like warehouse robots or delivery drones, to move through shared spaces without collisions. You input map layouts and the number of agents, and it outputs an optimized path plan, often visualized as an SVG animation. It's designed for anyone managing large fleets of robots in complex environments.
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