Multi-Agent Pathfinding

HELSA: Hierarchical Reinforcement Learning with Spatiotemporal Abstraction for Large-Scale Multi-Agent Path Finding

Proposed a hierarchical reinforcement learning framework for large-scale multi-agent pathfinding, addressing the challenges of sample inefficiency resulting from sparse rewards and partial observability.

Zhaoyi Song, Rongqing Zhang, Xiang Cheng

HELSA: Hierarchical Reinforcement Learning with Spatiotemporal Abstraction for Large-Scale Multi-Agent Path Finding