Anonymous Multi-Agent Path Finding (MAPF) with Conflict-Based Search and Space-Time A*

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.

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

[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 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.

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

Multi-Agent Pickup and Delivery implementation

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.

"When to Switch" Implementation: Addressing the PO-MAPF challenge with RePlan & EPOM policies. This repo includes search-based re-planning, reinforcement learning techniques, and three mixed policies for pathfinding in partially observable multi-agent environments. 🤖🛤️

Minimal Python implementation of PIBT for MAPF

Implementation of the SADG RHC feedback control scheme to reduce route completion times of delayed agents following MAPF plans.

Minimal Python implementation of LaCAM* for MAPF

[AAMAS 2024] HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding

SkyRover, a modular and extensible simulator tailored for cross-domain pathfinding research.

Combinatorial Decision Making and Optimization Course Project.

The POGEMA Toolbox is a comprehensive framework designed to facilitate the testing of learning-based approaches within the POGEMA environment. This toolbox offers a unified interface that enables the seamless execution of any learnable MAPF algorithm in POGEMA.

Multi Agent Path Finding CBS algorithm with visualization as a mini project in robotics seminar

An implementation of MAPF with visualization

Multi-agent pathfinding project

Pruning project for MAPF