Reinforcement learning achieved great success in domains ranging from games to complex control tasks. But reinforcement learning can go beyond specific tasks, and provide the foundation for building AI agents that can continually learn from interaction, in order to build knowledge and achieve goals.

In this talk, I will discuss the importance of rewards as a way to specify goals, and the way in which reinforcement learning can be used to learn general procedural and predictive knowledge. I will outline recent progress made in this area, and important open questions.