From How to learn to What to learn in Multiagent Systems and Robotics

There has been a lot of exciting recent progress on new and powerful machine learning algorithms and architectures: how to learn. But for autonomous agents acting in the dynamic, uncertain world, it is at least as important to be able to identify which concepts and subproblems to focus on: what to learn. This talk presents methods for identifying what to learn within the framework of reinforcement learning, focusing especially on applications in multiagent systems and robotics.