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

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.

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 settings.

This is a multi-agent path planning(also known as Multi-Agent Path Finding, MAPF) algorithm package for ROS

Iterative Refinement for Real-Time Multi-Robot Path Planning (IROS-21)

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

📍🗺️ A Python library for Multi-Agents Planning and Pathfinding (Centralized and Decentralized)

Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding (AIJ-22)

simple multi-agent pathfinding (MAPF) visualizer for research usage

Engineering LaCAM*: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding (AAMAS-24)

LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding (AAAI-23)

JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.

Multi-Agent Path Finding in C++ (WHCA*, iWHCA*, WCBS)

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 state-of-the-art solvers.

[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.

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 collision-free paths under partial observability without heuristics or agent communication. MAPF-GPT excels on unseen instances and outperforms state-of-the-art solvers.

Multi-Agent Pickup and Delivery implementation

[IROS 2024] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding