Published: April 23, 2021

Pieter Abbeel; Professor of Electrical Engineering and Computer Science; University of California, Berkeley

Unsupervised Reinforcement Learning

Deep reinforcement learning (Deep RL) has seen many successes, including learning to play Atari games, the classical game of Go, robotic locomotion and manipulation. However, past successes are ultimately in fairly narrow problem domains compared to the complexity of the real world. In this talk I will describe key limitations of existing deep reinforcement learning methods and discuss how advances in unsupervised representation learning and in unsupervised reinforcement learning could play a key role in solving more complex problems. Ultimately, of course, we need our AI agents to do the things we want them to do, and I’ll discuss recent progress on human-in-the-loop reinforcement learning, which empowers a human supervisor to teach an AI agent new skills without the usual extensive reward engineering or curriculum design efforts.