  In this article, the deep deterministic policy gradient (DDPG) method is used
to learn an optimal control policy of a multi-region state-dependent switched
system. We observe good performance of this model-free method and explain it in
a rigorous mathematical language. The performance of the learning-based methods
is compared with the optimal solution given by vanilla differential dynamic
programming (DDP) in three customized environments.
