  Self-healing capability is one of the most critical factors for a resilient
distribution system, which requires intelligent agents to automatically perform
restorative actions online, including network reconfiguration and reactive
power dispatch. These agents should be equipped with a predesigned decision
policy to meet real-time requirements and handle highly complex $N-k$
scenarios. The disturbance randomness hampers the application of
exploration-dominant algorithms like traditional reinforcement learning (RL),
and the agent training problem under $N-k$ scenarios has not been thoroughly
solved. In this paper, we propose the imitation learning (IL) framework to
train such policies, where the agent will interact with an expert to learn its
optimal policy, and therefore significantly improve the training efficiency
compared with the RL methods. To handle tie-line operations and reactive power
dispatch simultaneously, we design a hybrid policy network for such a
discrete-continuous hybrid action space. We employ the 33-node system under
$N-k$ disturbances to verify the proposed framework.
