Anonymous Multi-Agent Path Finding (MAPF) with Conflict-Based Search and Space-Time A*
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Updated
Oct 13, 2021 - Python
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)
Multi-Agent Pickup and Delivery implementation
[IROS'24] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
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
An implementation of MAPF with visualization
"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. 🤖🛤️
Implementation of some pathfinding algorithms
Multi Agent Path Finding CBS algorithm with visualization as a mini project in robotics seminar
MAPF instance generator
Combinatorial Decision Making and Optimization Course Project.
[AAMAS 2024] HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
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