Efficient Deep Learning for Multi Agent Pathfinding
Natalie Abreu · Proceedings of the AAAI Conference on Artificial Intelligence · 2022
Multi Agent Path Finding (MAPF) is widely needed to coordinate real-world robotic systems. New approaches turn to deep learning to solve MAPF instances, primarily using reinforcement learning, which has high computational costs. We propose a supervised learning approach to solve MAPF instances using a smaller, less costly model.