  Recently, deep reinforcement learning (DRL)-based approach has shown
promisein solving complex decision and control problems in power engineering
domain.In this paper, we present an in-depth analysis of DRL-based voltage
control fromaspects of algorithm selection, state space representation, and
reward engineering.To resolve observed issues, we propose a novel imitation
learning-based approachto directly map power grid operating points to effective
actions without any interimreinforcement learning process. The performance
results demonstrate that theproposed approach has strong generalization ability
with much less training time.The agent trained by imitation learning is
effective and robust to solve voltagecontrol problem and outperforms the former
RL agents.
