Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding

1 code implementation • 29 Sep 2021 • Lakshay Virmani, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset

By leveraging a Visual Transformer, we develop a learning-based single-agent planner, which plans for a single agent while paying attention to both the structure of the map and other agents with whom conflicts may happen.

Multi-Agent Path Finding