An Adversarial Objective for Scalable Exploration
Bernadette Bucher,Karl Schmeckpeper,Nikolai Matni,Kostas Daniilidis,Bernadette Bucher,Karl Schmeckpeper,Nikolai Matni,Kostas Daniilidis
Collecting new experience is costly in many robotic tasks, so determining how to efficiently explore in a new environment to learn as much as possible in as few trials as possible is an important problem for robotics. In this paper, we propose a method for exploring for the purpose of learning a dynamics model. Our key idea is to minimize a score given by a discriminator network as an objective fo...
