Multi-target coverage with connectivity maintenance at scale
remains challenging in current research. A novel double-observation pol-
icy learning framework (DOPLF) aiming at multi-agent system deploy-
ment in large scale coverage problems is proposed in this work. DOPLF
introduces observations from both global and local perspectives to en-
courage more e cient exploration in large scale coverage scenarios with
massive state spaces. Specifically, the local-level observation is derived
from target partition to provide regional target density for each agent,
and global-level observation provides an overall information of the en-
vironment. Both observations are then fed into the subsequent learn-
ing modules that primarily adopt graph attention network and proximal
policy optimization based reinforcement learning algorithm to generate
a distributed policy. Further, curriculum learning is applied to enhance
the model adaptability in larger team sizes. Finally, the proposed method
outperforms the baseline method in coverage rate and training e ciency
in simulations with the number of targets ranging from 50 to 500.