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.