📍🗺️ A Python library for Multi-Agents Planning and Pathfinding (Centralized and Decentralized)
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Updated
Oct 23, 2024 - Python
📍🗺️ A Python library for Multi-Agents Planning and Pathfinding (Centralized and Decentralized)
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.
This is the repo for the team Pikachu's solution in the League of Robot Competition 2023. Our solution won the Overall Best and Fast Mover tracks and ranked second in the Line Honours track.
[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.
Engineering LaCAM*: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding (AAMAS-24)
[IROS 2024] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
Implementation of some pathfinding and multi-agent pathfinding algorithms
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.
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.
[AAAI-2024] MATS-LP addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed approach utilizes a combination of Monte Carlo Tree Search and reinforcement learning for resolving conflicts.
Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding (AIJ-22)
Anonymous Multi-Agent Path Finding (MAPF) with Conflict-Based Search and Space-Time A*
Minimal Python implementation of LaCAM* for MAPF
Multi-Agent Pickup and Delivery implementation
Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding (IJCAI-23)
simple multi-agent pathfinding (MAPF) visualizer for research usage
Modern visualizer and simulator of the classic MAPF problem
Multi-agent pathfinding via Conflict Based Search
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.
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding (AAAI-23)
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