We explore differential dynamic programming for dynamical systems that form a directed graph structure. This planning method is applicable to complicated tasks where sub-tasks are sequentially connected and different skills are selected according to the situation. A pouring task is an example: it involves grasping and moving a container, and selection of skills, e.g. tipping and shaking. Our method can handle these situations; we plan the continuous parameters of each subtask and skill, as well as select skills. Our method is based on stochastic differential dynamic programming. We use stochastic neural networks to learn dynamical systems when they are unknown. Our method is a form of reinforcement learning. On the other hand, we use ideas from artificial intelligence, such as graph-structured dynamical systems, and frame-and-slots to represent a large state-action vector. This work is a partial unification of these different fields. We demonstrate our method in a simulated pouring task, where we show that our method generalizes over material property and container shape. Accompanying video: https://youtu.be/-ECmnG2BLE8.