HELSA: Hierarchical Reinforcement Learning with Spatiotemporal Abstraction for Large-Scale Multi-Agent Path Finding
Zhaoyi Song,Rongqing Zhang,Xiang Cheng,Zhaoyi Song,Rongqing Zhang,Xiang Cheng
The Multi-Agent Path Finding (MAPF) problem is a critical challenge in dynamic multi-robot systems. Recent studies have revealed that multi-agent reinforcement learning (MARL) is a promising approach to solving MAPF problems in a fully decentralized manner. However, as the size of the multi-robot system increases, sample inefficiency becomes a major impediment to learning-based methods. This paper...
