Deep Learning for Decision Making and Control

A remarkable feature of human and animal intelligence is the ability to autonomously acquire new behaviors. This research is concerned with designing algorithms that aim to bring this ability to robots and simulated characters. Levine will describe a class of guided policy search algorithms that tackle this challenge by transforming the task of learning control policies into a supervised learning problem, with supervision provided by simple, efficient trajectory-centric methods. Sergey Levine is a postdoc