AI Seminar sponsored by Apple

Benjamin Eysenbach -- Towards Autonomous Reinforcement Learning: Learning to Act with Less Human Supervision

Abstract: Widespread adoption of RL today is severely limited by its dependence on human supervision. In this talk, I'll discuss many areas where current approaches to RL requires human supervision. A couple recent projects make progress on this problem by partially removing the this dependence, enabling more autonomous RL. I'll conclude with some thoughts on how RL could be made even more autonomous.

This talk is largely based off the following papers:

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

Diversity is All You Need: Learning Skills without a Reward Function