  This paper focuses on the critical load restoration problem in distribution
systems following major outages. To provide fast online response and optimal
sequential decision-making support, a reinforcement learning (RL) based
approach is proposed to optimize the restoration. Due to the complexities
stemming from the large policy search space, renewable uncertainty, and
nonlinearity in a complex grid control problem, directly applying RL algorithms
to train a satisfactory policy requires extensive tuning to be successful. To
address this challenge, this paper leverages the curriculum learning (CL)
technique to design a training curriculum involving a simpler steppingstone
problem that guides the RL agent to learn to solve the original hard problem in
a progressive and more effective manner. We demonstrate that compared with
direct learning, CL facilitates controller training to achieve better
performance. To study realistic scenarios where renewable forecasts used for
decision-making are in general imperfect, the experiments compare the trained
RL controllers against two model predictive controllers (MPCs) using renewable
forecasts with different error levels and observe how these controllers can
hedge against the uncertainty. Results show that RL controllers are less
susceptible to forecast errors than the baseline MPCs and can provide a more
reliable restoration process.
