  With the growth of Renewable Energy (RE) generation, the operation of power
grids has become increasingly complex. One solution could be automated grid
operation, where Deep Reinforcement Learning (DRL) has repeatedly shown
significant potential in Learning to Run a Power Network (L2RPN) challenges.
However, only individual actions at the substation level have been subjected to
topology optimization by most existing DRL algorithms. In contrast, we propose
a more holistic approach by proposing specific Target Topologies (TTs) as
actions. These topologies are selected based on their robustness. As part of
this paper, we present a search algorithm to find the TTs and upgrade our
previously developed DRL agent CurriculumAgent (CAgent) to a novel topology
agent. We compare the upgrade to the previous CAgent and can increase their
L2RPN score significantly by 10%. Further, we achieve a 25% better median
survival time with our TTs included. Later analysis shows that almost all TTs
are close to the base topology, explaining their robustness
