The decision and control module plays a key role for autonomous driving, which is responsible for generating appropriate control commands that navigate the autonomous vehicles safely and efficiently. Existing decision and control modules for automated vehicles are mainly using a rule-based hand-engineered approach. Although working well in a number of specialized scenarios, such method shows its limitation when dealing with highly automated driving tasks such as dense urban scenarios. Recent advances in artificial intelligence have inspired a line of works about self-learning based decision and control, which enable self-reinforcement of the control policy to potentially super-human performance. In this chapter, we will introduce how to appropriately apply such techniques to automated vehicles. The chapter will begin with the motivations and basics, followed by the key challenges and recent achievements of self-learning decision and control for automated vehicles, focusing on the following key aspects, scalability, performance, interpretability, mixed-model, and emergency handling.