Efficient Deep Learning for Multi Agent Pathfinding

Natalie R Abreu

[AAAI-22] Undergraduate Consortium

Keywords
Poster Session 2 @ Blue 4, Poster Session 5 @ Blue 4, Poster Session 2, Poster Session 5

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Abstract: 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.

Sessions where this paper appears

  • Poster Session 2

    Fri, February 25 12:45 AM - 2:30 AM (+00:00)
    Blue 4
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  • Poster Session 5

    Sat, February 26 12:45 AM - 2:30 AM (+00:00)
    Blue 4
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