Anonymous Multi-Agent Path Finding (MAPF) with Conflict-Based Search and Space-Time A*
An Efficient Multi-Agent Path Finding Solver for Car-Like Robots
[ICLR-2025] POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF / MAPF settings.
Continuous CBS - a modification of conflict based search algorithm, that allows to perform actions (move, wait) of arbitrary duration. Timeline is not discretized, i.e. is continuous.
Iterative Refinement for Real-Time Multi-Robot Path Planning (IROS-21)
[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
Engineering LaCAM*: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding (AAMAS-24)
Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried…
Multi-agent pathfinding via Conflict Based Search
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding (AIJ-22)
simple multi-agent pathfinding (MAPF) visualizer for research usage
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding (AAAI-23)
A Python library for multi-agent planning and pathfinding: CBS, PIBT, LaCAM, LNS, space-time A*, plus decentralized NMPC, velocity obstacles and swarm behaviors
[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.
[AAAI-2024] Follower: This study addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed Follower approach utilizes a combination of a planning algorithm for constructing a long-term plan and reinforcement learning for resolving local conflicts.
Minimal Python implementation of PIBT for MAPF
[IROS 2024] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.
To associate your repository with the mapf topic, visit your repo's landing page and select "manage topics."