Peter Stone, UT at Austin - Talk Abstract

Abstract:

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 dynmaic, 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.