Test data for Map 1.

<div><p>An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. However, due to partial observability, agents often struggle to determine optimal strategies. Thus, d...

Full description

Saved in:

Bibliographic Details
Main Author:	Qingling Zhang (758713) (author)
Other Authors:	Peng Wang (34436) (author), Cui Ni (14773398) (author), Xianchang Liu (11260136) (author)
Published:	2025
Subjects:	Neuroscience Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified varying map sizes spatial dimensions based predicting future actions graph attention networks gated recurrent unit final action strategy experimental results show ensuring safe navigation determine optimal strategies convolutional neural network algorithm &# 8217 5 %, 73 agents often struggle agent path finding agent &# 8217 partial observability problem information fusion expands partial observability multiple agents fusion approach agent densities agent communication xlink "> time efficiency thereby enhancing scaled dot product aggregation performance enhancement paper explores overall performance observation range larger maps historical observations enable inter effectively solving effective multi continuous task aggregated data
Tags:	Add Tag No Tags, Be the first to tag this record!

Test data for Map 1.

Similar Items